Malicious attack detection and analysis

ABSTRACT

A system for characterizing malicious activity in an intelligent utility grid system includes a system storage in which to store a database including a plurality of rules. A collector is operable to collect and store in the system storage information-technology (IT) data including IT-related activity from the intelligent grid system. A complex event processing (CEP) bus is operable to receive non-IT data including location-specific event data from a plurality of electronic sources, the CEP bus further operable to disregard the non-IT data failing to meet a predetermined level of relevance to one of a plurality of risk-related events. A processor is operable to apply the plurality of rules to the relevant non-IT data to: associate an undesired event with reference to the IT-related activity; and determine a probability that the undesired event is indicative of malicious activity. The processor further applies a risk characterization to the undesired event based on the probability and the IT-related activity.

CLAIM OF PRIORITY

This application claims the benefit of priority of U.S. Provisional Application No. 61/346,787 filed on May 20, 2010, the entirety of which is hereby incorporated by reference.

BACKGROUND

1. Field of the Invention

The present invention relates generally to a system and method for detecting and identifying undesired events in intelligent utility grid systems, and more particularly to a system and method for detecting and identifying malicious attacks on an intelligent utility grid system.

2. Related Art

The objective of Internet Protocol (IP), Industrial Control System (ICS), Physical Control System, and Supervisory Control and Data Acquisition (SCADA) attacks on the Smart Grid is to bypass the grid's normal operation by exploiting one or more weaknesses (e.g., Radio Frequency (RF) jamming wireless nodes on the grid, key derivation, flashing firmware, anonymous inputs from inappropriate entities, physical hardware tampering). Many of these risks have well-defined solutions that are addressed by either ICS/SCADA security controls, physical security controls, or enterprise information technology (IT), operational, or physical security controls. Residual Risk is the remaining risk after the security controls have been applied. Most systems are never fully secure, and residual risk always remains. When faced with the cyber-security challenges of the smart grid, a residual security risk remains that is beyond the typical risks mitigated by SCADA, enterprise IT, operational, or physical security controls.

BRIEF SUMMARY

According to one aspect of the disclosure, a method of characterizing malicious activity in an intelligent utility grid system may include receiving information-technology (IT) data including IT-related activity from the intelligent grid system. The method may further include receiving non-IT data including location-specific event data from a plurality of electronic sources. The non-IT data, as will be explained in more detail later, includes information-technology-related information (or data) that is not traditionally used by a power grid as well as information such as historical data and addresses or locations of high value targets. The method may further include pre-processing the non-IT data including: disregarding the non-IT data failing to meet a predetermined level of relevance to one of a plurality of risk-related events. The method may further include applying a plurality of rules to the pre-processed non-IT data to: associate an undesired event with reference to the IT-related activity; and determine a probability that the undesired event is indicative of malicious activity. The event may have already occurred or may not yet have occurred. The method may further include applying a risk characterization to the undesired event based on the probability and the IT-related activity.

According to another aspect of the disclosure, a system for characterizing malicious activity in an intelligent utility grid may include system storage in which to store a database including a plurality of rules. A collector may be operable to collect and store in the system storage information-technology (IT) data including IT-related activity from the intelligent grid system. A complex event processing (CEP) bus may be operable to receive non-IT data including location-specific event data from a plurality of electronic sources, the CEP bus further operable to disregard the non-IT data failing to meet a predetermined level of relevance to one of a plurality of risk-related events. A processor may be operable to apply the plurality of rules to the relevant non-IT data to: associate an undesired event with reference to the IT-related activity; and determine a probability that the undesired event is indicative of malicious activity. The processor may further be operable to apply a risk characterization to the undesired event based on the probability and the IT-related activity. The risk characterization may include, for instance, an engineering risk for which a maintenance team may be dispatched or it may be a security risk for which law enforcement authorities are alerted.

Other systems, methods, features and advantages will be, or will become, apparent to one with skill in the art upon examination of the following figures and detailed description. It is intended that all such additional systems, methods, features and advantages be included within this description, be within the scope of the invention, and be protected by the following claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of one example of the overall architecture for a power grid.

FIG. 2 is a block diagram of an Intelligent Network Data Enterprise (INDE) CORE depicted in FIG. 1.

FIG. 3 is a block diagram of another example of the overall architecture for a power grid.

FIG. 4 is a block diagram of the INDE SUBSTATION depicted in FIGS. 1 and 3.

FIG. 5 is a block diagram of the INDE DEVICE depicted in FIGS. 1 and 3.

FIG. 6 is a block diagram of still another example of the overall architecture for a power grid.

FIG. 7 is a block diagram of still another example of the overall architecture for a power grid.

FIG. 8 is a block diagram including a listing of some examples of observability processes.

FIG. 9 illustrates a flow diagram of Grid State Measurement & Operations processes.

FIG. 10 illustrates a flow diagram of Non-Operational Data processes.

FIG. 11 illustrates a flow diagram of Event Management processes.

FIG. 12 illustrates a flow diagram of Demand Response (DR) Signaling processes.

FIG. 13 illustrates a flow diagram of Outage Intelligence processes.

FIG. 14 illustrates a flow diagram of Fault Intelligence processes.

FIG. 15 illustrates a flow diagram of Meta-data Management processes.

FIG. 16 illustrates a flow diagram of Notification Agent processes.

FIG. 17 illustrates a flow diagram of Collecting Meter Data (AMI) processes.

FIGS. 18A-D are an example of an entity relationship diagram, which may be used to represent a baseline connectivity database.

FIG. 19 illustrates an example of a blueprint progress flow graphic.

FIG. 20 is a block diagram of an example risk assessment system.

FIG. 21 is a block diagram of an example of an intelligent utility grid.

FIG. 22 is an operational flow diagram of an example risk assessment system.

DETAILED DESCRIPTION

By way of overview, the preferred embodiments described below relate to a method and system for managing a power grid. As discussed in more detail below, certain aspects relate to the power grid itself (including hardware and software in the electric power transmission and/or the electricity distribution). Further, certain aspects relate to the functional capabilities of the central management of the power grid. These functional capabilities may be grouped into two categories, operation and application. The operations services enable the utilities to monitor and manage the smart grid infrastructure (such as applications, network, servers, sensors, etc). The method and system disclosed herein are related to the following patent applications, which are incorporated herein by reference in their entireties: U.S. patent application Ser. No. 12/378,102 (published as U.S. Published Application No. 2009/0281674 A1); and U.S. patent application Ser. No. 12/378,091 (published as U.S. Published Application No. 2009/0281673 A1).

As discussed in more detail below, the application capabilities may relate to the measurement and control of the grid itself. Specifically, the application services enable the functionality that may be important to a smart grid, and may include: (1) data collection processes; (2) data categorization and persistence processes; and (3) observability processes. As discussed in more detail below, using these processes allows one to “observe” the grid, analyze the data and derive information about the grid.

In one embodiment, a utility system may include an intelligent security system to avoid malicious attacks on the intelligence of a utility system to cause undesired results. In order to avoid such malicious attacks, or to at least detect such an attack quickly, the utility system needs to be able to detect and characterize undesired events, thus to categorize such events in a useful way. One such utility system is an electrical Smart Grid utility system. Attacks may include attacks on Internet Protocol (IP) and supervisory control and data acquisition (SCADA) systems and may have particular objectives. Such objects may include bypassing the Smart Grid's normal operation by exploiting one or more weaknesses, such as through Radio Frequency (RF) jamming wireless nodes on the grid, key derivation, anonymous inputs from inappropriate entities, physical hardware tampering. Many of these issues have solutions that are addressed by either SCADA security controls or enterprise and operational security controls. “Residual risk” may be considered the remaining risk after the security controls have been applied. When faced with the cyber-security challenges of the Smart Grid, a residual security risk remains that may be beyond the typical risks mitigated by SCADA, enterprise IT, operational, or physical security controls.

In one embodiment, an incident event (I) may be the occurrence of an undesired event. The Probability of an Incident (P(I)) may be the possibility or chance that the occurrence of incidents is based on the existence of a threat and existence of a vulnerability that can be exploited by the threat. A Threat Event (T) may be an intentional malicious attack or an unexpected hazard. Equation 1 below is for the probability of the occurrence of an Incident as the sum of the product of the probability of the occurrence of a malicious attack (P(A)) and the existence of the vulnerability for each attack and the probability of the occurrence of an unexpected hazard (P(H)) plus the probability of a vulnerability associated with each unexpected hazard. Equation 1 is the probability of an incident resulting from mutually independent events, e.g., mutually independent Intentional Attacks (A) and Unexpected Hazards (H). P(I)=Π_(i) P(A _(i))*Π_(j) P(H _(j))  Eqn. 1 where i, j are whole numbers.

The Residual Risk (RR) may be the probabilistic result of security controls not mitigating intentional malicious attacks and unexpected hazards. Equation 2 is the residual risk (RR) of the intentional or unintentional security incident occurring due to a lack of adequate protections (“Controls Gaps”), i.e. the risk that security controls will not prevent, detect and protect from independent Intentional Attacks and Unexpected Hazard events. RR=P(I)*P(Controls Gap)  Eqn. 2

With Equation 1, the probability of a security incident may be derived with enterprise, operations, and ICS. Equations 4 through 6 depict these equations. P(I _(EnterpriseIT))=Π_(i) P(A _(i,EnterpriseIT))*Π_(j) P(H _(j EnterpriseIT))  Eqn. 3 P(I _(OperationIT))=Π_(i) P(A _(i,OperationIT))*Π_(j) P(H _(j,OperationIT))  Eqn. 4 P(I _(SCADA IT))=Π_(i) P(A _(i,SCADA))*Π_(j) P(H _(j,SCADA))  Eqn. 5 RR=P(I)*P(Controls Gap)  Eqn. 6

Equation 7 shows that the probability of an incident on the smart grid is the sum of the probability of the sources of disjoint threat events, e.g., enterprise, operational, SCADA, and residual. P(I _(smart Grid))=P(I _(Enterprise))+P(I _(Operational))+P(I _(SCADA))+P(I _(Residual))  Eqn. 7 where, attack (A), hazard (H), Vulnerability (V), and Residual Risk (R) are mutually exclusive.

The vulnerabilities in Equations 3 through 5 may be addressed through passive traditional enterprise IT security, e.g., leveraging header or packet inspection and typically use the Catalyst Switched Port Analyzer (SPAN) port or Mirror port (e.g., Intrusion Detection System (IDS), Content Filter), Active signature based traditional IT security (e.g., Firewall), Active and Passive Smart Grid operational IT security (e.g., Hardened Advanced Metering Infrastructure components, Smart Grid Threat Definition files for an IDS, automated policy managers for NERC-CIP compliance), and SCADA security. A sample of the vulnerabilities that are accounted for may include:

Header Information

Packet Contents

SCADA Anomalies

Communications Protocols

Source & Target IP Addresses

Source & Target TCP and UDP ports

Direction of Initiation of Communications

Volume of Different Kinds of Traffic

Packet Formats and Content

Implementing enterprise and operational defense-in-depth, layered security approaches in addition to SCADA security may make the probability of an incident negligible. As a result, Equation 7 may be simplified to Equation 8 as follows: P(I _(Smart Grid))=P(I _(Residual))+δ(I*)<<P(I _(Smart Grid))+δ(I _(Operational IT))+δ(I _(SCADA))  Eqn. 8

Because Computer Emergency Response Teams (CERT) and other IT security monitoring organizations are not designed to collect information for control systems, control system security must rely on institutional knowledge of its competitor's control systems security monitoring data. Access to this data is not typically shared because public knowledge of security breaches would have adverse effects on investor confidence and the company's valuation. A Host Intrusion Detection System (HIDS) leverages software agents installed on hosts to monitor the hosts for anomalous behavior. Unfortunately, due to their proprietary, real-time operating systems (OSs) or embedded OSs, not all smart grid components can have a HIDS installed on them, e.g., SCADA components that measure, control, and monitor electrical processes. Thus, residual risk includes the lack of visibility that a HIDS would have accounted for in addition to the vulnerabilities associated with unintentional events. Vulnerabilities not accounted for are associated with: acts of nature, lack of capacity; unique protocols not recognized by IDS and security information and event managers (SIEMs) (e.g., Modbus TCP, DNP3, ODVA Ethernet/IP, ICCP); false negatives (e.g., an outage can misdiagnosed as a security event); access control for remote nodes (RTUs and PLCs); integrity and anomaly checks on proprietary nodes; proprietary real-time OS or embedded OSS; and forensic limitations due to interoperability.

In one embodiment, a Complex Event Processor (or Processing) (CEP) bus may filter, process, and correlate data from various sources and abstract events that might occur in the future or have taken place in the past such as energy system failure or anomalies. To reduce the probability of residual risk, the CEP bus may be tuned with utility rules, such as electric utility rules, to detect and filter out anomalies not realized in an IDS, antivirus, or firewall. The inputs to the systems should be all data that influences the availability, integrity and confidentiality of the Grid. Examples of inputs from various electronic sources include:

Weather Feeds

Disturbance Recorders

Digital Fault Recorders (Oscilography)

Harmonic Recorders

Power Quality Monitoring

Device Status

Connectivity State

Control Limit

US CERT Feeds

Constituent Feeds

GPS

RF Interference

Power Management Unit (PMU)

Sensors

Load Forecasts

Renewable Generation Forecasts

For example, weather feeds may be used to predict outages and brown outs due to a surge in use (e.g., high temperatures leading to rise in use of power for air-conditioning units). Also, CERT reports and other Energy companies can collaborate and share incident report databases which could be fed into their CEP bus to identify anomalies that are caused by misuse or system compromise.

In one embodiment, occurrence of undesired events associated with the residual risk may be detected and determined through implementation of a SIEM to a Smart Grid system allowing the CEP and SIEM to identify malicious attacks as a likely cause of an undesired event based on data other than information-technology (IT) logs (e.g., Netflow, Syslog, Vflow, JFlow, SFlow, SNMP traps, LDAP data). This data may include some IT data that is non-traditional and normally not analyzed with reference to security controls for a power grid. For instance, it may include grid analog measurements such as phasor measurements from a PMU, Global Positioning System (GPS) coordinates, addresses or geographic locations of high-valued targets, or vehicular accident reports.

INDE High Level Architecture Description

Overall Architecture

Turning to the drawings, wherein like reference numerals refer to like elements, FIG. 1 illustrates one example of an overall architecture for INDE. This architecture may serve as a reference model that provides for end-to-end collection, transport, storage, and management of smart grid data; it may also provide analytics and analytics management, as well as integration of the forgoing into utility processes and systems. Hence, this architecture may be viewed as an enterprise-wide architecture. Certain elements, such as operational management and aspects of the grid itself, are discussed in more detail below.

The architecture depicted in FIG. 1 may include up to four data and integration buses: (1) a high speed sensor data bus 146 (which may include operational and non-operational data); (2) a dedicated event processing bus 147 (which may include event data); (3) an operations service bus 130 (which may serve to provide information about the smart grid to the utility back office applications); and (4) an enterprise service bus for the back office IT systems (shown in FIG. 1 as the enterprise integration environment bus 114 for serving enterprise IT 115). The separate data buses may be achieved in one or more ways. For example, two or more of the data buses, such as the high speed sensor data bus 146 and the event processing bus 147, may be different segments in a single data bus. Specifically, the buses may have a segmented structure or platform. As discussed in more detail below, hardware and/or software, such as one or more switches, may be used to route data on different segments of the data bus.

As another example, two or more of the data buses may be on separate buses, such as separate physical buses in terms of the hardware needed to transport data on the separate buses. Specifically, each of the buses may include cabling separate from each other. Further, some or all of the separate buses may be of the same type. For example, one or more of the buses may comprise a local area network (LAN), such as Ethernet® over unshielded twisted pair cabling and Wi-Fi. As discussed in more detail below, hardware and/or software, such as a router, may be used to route data on data onto one bus among the different physical buses.

As still another example, two or more of the buses may be on different segments in a single bus structure and one or more buses may be on separate physical buses. Specifically, the high speed sensor data bus 146 and the event processing bus 147 may be different segments in a single data bus, while the enterprise integration environment bus 114 may be on a physically separate bus.

Though FIG. 1 depicts four buses, fewer or greater numbers of buses may be used to carry the four listed types of data. For example, a single unsegmented bus may be used to communicate the sensor data and the event processing data (bringing the total number of buses to three), as discussed below. And, the system may operate without the operations service bus 130 and/or the enterprise integration environment bus 114.

The IT environment may be SOA-compatible. Service Oriented Architecture (SOA) is a computer systems architectural style for creating and using business processes, packaged as services, throughout their lifecycle. SOA also defines and provisions the IT infrastructure to allow different applications to exchange data and participate in business processes. Although, the use of SOA and the enterprise service bus are optional.

The Figures illustrate different elements within the overall architecture, such as the following: (1) INDE CORE 120; (2) INDE SUBSTATION 180; and (3) INDE DEVICE 188. This division of the elements within the overall architecture is for illustration purposes. Other division of the elements may be used. The INDE architecture may be used to support both distributed and centralized approaches to grid intelligence, and to provide mechanisms for dealing with scale in large implementations. Distributed analysis and culling of event-related data may be pushed to the edges of the grid, for instance to electric meters, to the extent that the grid-based structure includes the computing capability to perform whatever processing tasks required at those points in the grid.

The INDE Reference Architecture is one example of the technical architecture that may be implemented. For example, it may be an example of a meta-architecture, used to provide a starting point for developing any number of specific technical architectures, one for each utility solution, as discussed below. Thus, the specific solution for a particular utility may include one, some, or all of the elements in the INDE Reference Architecture. And, the INDE Reference Architecture may provide a standardized starting point for solution development. Discussed below is the methodology for determining the specific technical architecture for a particular power grid.

The INDE Reference Architecture may be an enterprise wide architecture. Its purpose may be to provide the framework for end to end management of grid data and analytics and integration of these into utility systems and processes. Since smart grid technology affects every aspect of utility business processes, one should be mindful of the effects not just at the grid, operations, and customer premise levels, but also at the back office and enterprise levels. Consequently, the INDE Reference Architecture can and does reference enterprise level SOA, for example, in order to support the SOA environment for interface purposes. This should not be taken as a requirement that a utility must convert their existing IT environment to SOA before a smart grid can be built and used. An enterprise service bus is a useful mechanism for facilitating IT integration, but it is not required in order to implement the rest of the smart grid solution. The discussion below focuses on different components of the INDE smart grid elements.

INDE Component Groups

As discussed above, the different components in the INDE Reference Architecture may include, for example: (1) INDE CORE 120; (2) INDE SUBSTATION 180; and (3) INDE DEVICE 188. The following sections discuss these three example element groups of the INDE Reference Architecture and provide descriptions of the components of each group.

INDE CORE

FIG. 2 illustrates the INDE CORE 120, which is the portion of INDE Reference Architecture that may reside in an operations control center, as shown in FIG. 1. The INDE CORE 120 may contain a unified data architecture for storage of grid data and an integration schema for analytics to operate on that data. This data architecture may use the International Electrotechnical Commission (IEC) Common Information Model (CIM) as its top level schema. The IEC CIM is a standard developed by the electric power industry that has been officially adopted by the IEC, aiming to allow application software to exchange information about the configuration and status of an electrical network.

In addition, this data architecture may make use of federation middleware 134 to connect other types of utility data (such as, for example, meter data, operational and historical data, log and event files), and connectivity and meta-data files into a single data architecture that may have a single entry point for access by high level applications, including enterprise applications. Real time systems may also access key data stores via the high speed data bus and several data stores can receive real time data. Different types of data may be transported within one or more buses in the smart grid. As discussed below in the NDE SUBSTATION 180 section, substation data may be collected and stored locally at the substation. Specifically, a database, which may be associated with and proximate to the substation, may store the substation data. Analytics pertaining to the substation level may also be performed at the substation computers and stored at the substation database, and all or part of the data may be transported to the control center.

The types of data transported may include operation and non-operational data, events, grid connectivity data, and network location data. Operational data may include, but is not limited to, switch state, feeder state, capacitor state, section state, meter state, FCI state, line sensor state, voltage, current, real power, reactive power, etc. Non-operational data may include, but is not limited to, power quality, power reliability, asset health, stress data, etc. The operational and non-operational data may be transported using an operational/non-operational data bus 146. Data collection applications in the electric power transmission and/or electricity distribution of the power grid may be responsible for sending some or all of the data to the operational/non-operational data bus 146. In this way, applications that need this information may be able to get the data by subscribing to the information or by invoking services that may make this data available.

Events may include messages and/or alarms originating from the various devices and sensors that are part of the smart grid, as discussed below. Events may be directly generated from the devices and sensors on the smart grid network as well as generated by the various analytics applications based on the measurement data from these sensors and devices. Examples of events may include meter outage, meter alarm, transformer outage, etc. Grid components like grid devices (smart power sensors such as a sensor with an embedded processor that can be programmed for digital processing capability, temperature sensors, etc.), power system components that include additional embedded processing (RTUs, etc), smart meter networks (meter health, meter readings, etc), and mobile field force devices (outage events, work order completions, etc) may generate event data, operational and non-operational data. The event data generated within the smart grid may be transmitted via an event bus 147.

Grid connectivity data may define the layout of the utility grid. There may be a base layout which defines the physical layout of the grid components (sub stations, segments, feeders, transformers, switches, reclosers, meters, sensors, utility poles, etc.) and their inter-connectivity at installation. Based on the events within the grid (component failures, maintenance activity, etc.), the grid connectivity may change on a continual basis. As discussed in more detail below, the structure of how the data is stored as well as the combination of the data enable the historical recreation of the grid layout at various past times. Grid connectivity data may be extracted from the Geographic Information System (GIS) on a periodic basis as modifications to the utility grid are made and this information is updated in the GIS application.

Network location data may include the information about the grid component on the communication network. This information may be used to send messages and information to the particular grid component. Network location data may be either entered manually into the Smart Grid database as new Smart Grid components are installed or is extracted from an Asset Management System if this information is maintained externally.

As discussed in more detail below, data may be sent from various components in the grid (such as INDE SUBSTATION 180 and/or INDE DEVICE 188). The data may be sent to the INDE CORE 120 wirelessly, wired, or a combination of both. The data may be received by utility communications networks 160, which may send the data to routing device 190. Routing device 190 may comprise software and/or hardware for managing routing of data onto a segment of a bus (when the bus includes a segmented bus structure) or onto a separate bus. Routing device 190 may comprise one or more switches or a router. Routing device 190 may comprise a networking device whose software and hardware routes and/or forwards the data to one or more of the buses. For example, the routing device 190 may route operational and non-operational data to the operational/non-operational data bus 146. The router may also route event data to the event bus 147.

The routing device 190 may determine how to route the data based on one or more methods. For example, the routing device 190 may examine one or more headers in the transmitted data to determine whether to route the data to the segment for the operational/non-operational data bus 146 or to the segment for the event bus 147. Specifically, one or more headers in the data may indicate whether the data is operation/non-operational data (so that the routing device 190 routes the data to the operational/non-operational data bus 146) or whether the data is event data (so that the routing device 190 routes the event bus 147). Alternatively, the routing device 190 may examine the payload of the data to determine the type of data (e.g., the routing device 190 may examine the format of the data to determine if the data is operational/non-operational data or event data).

One of the stores, such as the operational data warehouse 137 that stores the operational data, may be implemented as true distributed database. Another of the stores, the historian (identified as historical data 136 in FIGS. 1 and 2), may be implemented as a distributed database. The other “ends” of these two databases may be located in the INDE SUBSTATION 180 group (discussed below). Further, events may be stored directly into any of several data stores via the complex event processing bus. Specifically, the events may be stored in event logs 135, which may be a repository for all the events that have published to the event bus 147. The event log may store one, some, or all of the following: event id; event type; event source; event priority; and event generation time. The event bus 147 need not store the events long term, providing the persistence for all the events.

The storage of the data may be such that the data may be as close to the source as possible or practicable. In one implementation, this may include, for example, the substation data being stored at the INDE SUBSTATION 180. But this data may also be required at the operations control center level 116 to make different types of decisions that consider the grid at a much granular level. In conjunction with a distributed intelligence approach, a distributed data approach may be been adopted to facilitate data availability at all levels of the solution through the use of database links and data services as applicable. In this way, the solution for the historical data store (which may be accessible at the operations control center level 116) may be similar to that of the operational data store. Data may be stored locally at the substation and database links configured on the repository instance at the control center, provide access to the data at the individual substations. Substation analytics may be performed locally at the substation using the local data store. Historical/collective analytics may be performed at the operations control center level 116 by accessing data at the local substation instances using the database links. Alternatively, data may be stored centrally at the INDE CORE 120. However, given the amount of data that may need to be transmitted from the INDE DEVICES 188, the storage of the data at the INDE DEVICES 188 may be preferred. Specifically, if there are thousands or tens of thousands of substations (which may occur in a power grid), the amount of data that needs to be transmitted to the INDE CORE 120 may create a communications bottleneck.

Finally, the INDE CORE 120 may program or control one, some or all of the INDE SUBSTATION 180 or INDE DEVICE 188 in the power grid (discussed below). For example, the INDE CORE 120 may modify the programming (such as download an updated program) or provide a control command to control any aspect of the INDE SUBSTATION 180 or INDE DEVICE 188 (such as control of the sensors or analytics). Other elements, not shown in FIG. 2, may include various integration elements to support this logical architecture.

Table 1 describes the certain elements of INDE CORE 120 as depicted in FIG. 2.

TABLE 1 INDE CORE Elements INDE CORE Element Description CEP Services 144 Provides high speed, low latency event stream processing, event filtering, and multi-stream event correlation Centralized Grid Analytics May consist of any number of commercial or custom Applications 139 analytics applications that are used in a non-real time manner, primarily operating from the data stores in CORE Visualization/Notification Support for visualization of data, states and event streams, Services 140 and automatic notifications based on event triggers Application Management Services (such as Applications Support Services 142 and Services 141 Distributed Computing Support 143) that support application launch and execution, web services, and support for distributed computing and automated remote program download (e.g., OSGi) Network Management Automated monitoring of communications networks, Services 145 applications and databases; system health monitoring, failure root cause analysis (non-grid) Grid Meta-Data Services 126 Services (such as Connectivity Services 127, Name Translation 128, and TEDS Service 129) for storage, retrieval, and update of system meta-data, including grid and communication/sensor net connectivity, point lists, sensor calibrations, protocols, device set points, etc. Grid Data/Analytics Services Services (such as Sensor Data Services 124 and Analytics 123 Management Services 125) to support access to grid data and grid analytics; management of analytics Meter Data Management Meter data management system functions (e.g., Lodestar) System 121 AMOS Meter Data Services See discussion below Real Time Complex Event Message bus dedicated to handling event message streams - Processing Bus 147 purpose of a dedicated bus is to provide high bandwidth and low latency for highly bursty event message floods. The event message may be in the form of XML message. Other types of messages may be used. Events may be segregated from operational/non- operational data, and may be transmitted on a separate or dedicated bus. Events typically have higher priority as they usually require some immediate action from a utility operational perspective (messages from defective meters, transformers, etc). The event processing bus (and the associated event correlation processing service depicted in FIG. 1) may filter floods of events down into an interpretation that may better be acted upon by other devices. In addition, the event processing bus may take multiple event streams, find various patterns occurring across the multiple event streams, and provide an interpretation of multiple event streams. In this way, the event processing bus may not simply examine the event data from a single device, instead looking at multiple devices (including multiple classes of devices that may be seemingly unrelated) in order to find correlations. The analysis of the single or multiple event streams may be rule based. Real Time Op/Non-Op Data Operational data may include data reflecting the current Bus 146 electrical state of the grid that may be used in grid control (e.g., currents, voltages, real power, reactive power, etc.). Non-operational data may include data reflecting the “health” or condition of a device. Operational data has previously been transmitted directly to a specific device (thereby creating a potential “silo” problem of not making the data available to other devices or other applications). For example, operational data previously was transmitted to the SCADA (Supervisory Control And Data Acquisition) system for grid management (monitor and control grid). However, using the bus structure, the operational data may also be used for load balancing, asset utilization/optimization, system planning, etc., as discussed for example in FIGS. 10-19. Non-operational data was previously obtained by sending a person in the field to collect the operational data (rather than automatically sending the non-operational data to a central repository). Typically, the operational and non-operational data are generated in the various devices in the grid at predetermined times. This is in contrast to the event data, which typically is generated in bursts, as discussed below. A message bus may be dedicated to handling streams of operational and non-operational data from substations and grid devices. The purpose of a dedicated bus may be to provide constant low latency throughput to match the data flows; as discussed elsewhere, a single bus may be used for transmission of both the operational and non-operational data and the event processing data in some circumstances (effectively combining the operational/non-operational data bus with the event processing bus). Operations Service Bus 130 Message bus that supports integration of typical utility operations applications (EMS (energy management system), DMS (distribution management system), OMS (outage management system), GIS (geographic information system), dispatch) with newer smart grid functions and systems (DRMS (demand response management system), external analytics, CEP, visualization). The various buses, including the Operational/Non-operational Data bus 146, the Event data bus 147, and the operations Service Bus 130 may obtain weather feeds, etc. via a security framework 117. The operations service bus 130 may serve as the provider of information about the smart grid to the utility back office applications, as shown in FIG. 1. The analytics applications may turn the raw data from the sensors and devices on the grid into actionable information that will be available to utility applications to perform actions to control the grid. Although most of the interactions between the utility back office applications and the INDE CORE 120 is expected to occur via this bus, utility applications will have access to the other two buses and will consume data from those buses as well (for example, meter readings from the op/non-op data bus 146, outage events from the event bus 147). CIM Data Warehouse 132 Top level data store for the organization of grid data; uses the IEC CIM data schema; provides the primary contact point for access to grid data from the operational systems and the enterprise systems. Federation Middleware allow communication to the various databases. Connectivity Warehouse 131 The connectivity warehouse 131 may contain the electrical connectivity information of the components of the grid. This information may be derived from the Geographic Information System (GIS) of the utility which holds the as-built geographical location of the components that make up the grid. The data in the connectivity warehouse 131 may describe the hierarchical information about all the components of the grid (substation, feeder, section, segment, branch, t-section, circuit breaker, recloser, switch, etc. - basically all the assets). The connectivity warehouse 131 may have the asset and connectivity information as built. Thus, the connectivity warehouse 131 may comprise the asset database that includes all the devices and sensors attached to the components of the grid. Meter Data Warehouse 133 The meter data warehouse 133 may provide rapid access to meter usage data for analytics. This repository may hold all the meter reading information from the meters at customer premises. The data collected from the meters may be stored in meter data warehouse 133 and provided to other utility applications for billing (or other back- office operations) as well as other analysis. Event Logs 135 Collection of log files incidental to the operation of various utility systems. The event logs 135 may be used for post mortem analysis of events and for data mining. Historical Data 136 Telemetry data archive in the form of a standard data historian. Historical data 136 may hold the time series non-operational data as well as the historical operational data. Analytics pertaining to items like power quality, reliability, asset health, etc. may be performed using data in historical data 136. Additionally, as discussed below, historical data 136 may be used to derive the topology of the grid at any point in time by using the historical operational data in this repository in conjunction with the as-built grid topology stored in the connectivity data mart. Further, the data may be stored as a flat record, as discussed below. Operational Data 137 Operational Data 137 may comprise a real time grid operational database. Operational Data 137 may be built in true distributed form with elements in the substations (with links in Operational Data 137) as well as the Operations center. Specifically, the Operational Data 137 may hold data measurements obtained from the sensors and devices attached to the grid components. Historical data measurements are not held in this data store, instead being held in historical data 136. The data base tables in the Operational Data 137 may be updated with the latest measurements obtained from these sensors and devices. DFR/SER Files 138 Digital fault recorder and serial event recorder files used for event analysis and data mining; files generally are created in the substations by utility systems and equipment.

As discussed in Table 1, the real time data bus 146 (which communicates the operational and non-operational data) and the real time complex event processing bus 147 (which communicates the event processing data) are combined into a single bus 346. An example of this is illustrated in the block diagram 300 in FIG. 3.

As shown in FIG. 1, the buses are separate for performance purposes. For CEP processing, low latency may be important for certain applications which are subject to very large message bursts. Most of the grid data flows, on the other hand, are more or less constant, with the exception of digital fault recorder files, but these can usually be retrieved on a controlled basis, whereas event bursts are asynchronous and random.

FIG. 1 further shows additional elements in the operations control center 116 separate from the INDE CORE 120. Specifically, FIG. 1 further shows Meter Data Collection Head End(s) 153, a system that is responsible for communicating with meters (such as collecting data from them and providing the collected data to the utility). Demand Response Management System 154 is a system that communicates with equipment at one or more customer premises that may be controlled by the utility. Outage Management System 155 is a system that assists a utility in managing outages by tracking location of outages, by managing what is being dispatched, and by how they are being fixed. Energy Management System 156 is a transmission system level control system that controls the devices in the substations (for example) on the transmission grid. Distribution Management System 157 is a distribution system level control system that controls the devices in the substations and feeder devices (for example) for distribution grids. IP Network Services 158 is a collection of services operating on one or more servers that support IP-type communications (such as DHCP and FTP). Dispatch Mobile Data System 159 is a system that transmits/receives messages to mobile data terminals in the field. Circuit & Load Flow Analysis, Planning, Lightning Analysis and Grid Simulation Tools 152 are a collection of tools used by a utility in the design, analysis and planning for grids. IVR (integrated voice response) and Call Management 151 are systems to handle customer calls (automated or by attendants). Incoming telephone calls regarding outages may be automatically or manually entered and forwarded to the Outage Management System 155. Work Management System 150 is a system that monitors and manages work orders. Geographic Information System 149 is a database that contains information about where assets are located geographically and how the assets are connected together. If the environment has a Services Oriented Architecture (SOA), Operations SOA Support 148 is a collection of services to support the SOA environment.

In one embodiment, the STEM 162 included in the INDE core 120 may be used in conjunction with the CEP processing bus 147 to detect and determine occurrences of undesired events associated with residual risk and determine if such occurrences are likely the result of a malicious attack. Referring to FIG. 20, various types of data 2000 may be received by the CEP bus 147. Table 2 below provides a non-exhaustive list of an example of types of information that may be used as data 2000 and where the data may originate from electronic sources 2001 within the INDE system of FIG. 1.

TABLE 2 Data Types Power outages Vehicle in other Traffic Epidemiology Police Regions/ Seismology Feeds (e.g., (e.g., Google Reports Countries Reports satellite) Flu Trends) Homeland Twitter Weather Disturbance Digital Fault Security Trending Feeds Recorders Recorders Advisory Statistics (Oscillography) System Harmonic Power Quality Device Connectivity Control Limit Recorders Monitoring Status State US CERT Constituent GPS RF Interface PMU Feeds Feeds Sensors Load forecasts Renewable Google Physical generation Alerts Security Logs forecasts

In one embodiment, the data 2000 may be considered non-IT log data or dynamic, environment specific, trend data (altogether referred to herein as “non-IT data” for simplicity). Accordingly, the non-IT data 2000 may include, but is not limited to, non-traditional IT information such as phasor measurements, connectivity states, and outages in other regions as shown in Table 2; location-specific event data as shown in Table 2; historical data that can show past trends or usual behavior; and geographic location data (such as data related to specific locations, such as notable or high-value targets such as the Pentagon, the Federal Reserve, the White House, Army bases, foreign embassies, etc.). The data 2000 may be received through various electronic sources 2001 such as sensors and detectors within INDE, network-based data, internet-based data, or other types of suitable data. A non-exhaustive list of electronic sources 2001 of non-IT data may include: a Web-crawling device; a search engine-capable computing device; a Web-access device; a GPS device; a social-media-thread monitoring device; a thermometer; and an emergency response communicator. These electronic sources 2001 may be coupled with the CEP bus 147. Herein, the phrase “coupled with” is defined to mean directly connected to or indirectly connected through one or more intermediate components. Such intermediate components may include both hardware and software based components.

A number of tools may be understood as useable by or with reference to the electronic sources 2001, including but not limited to social-media-capable devices that scour Tweets, Facebook posts, or other social media streams for location-based discussions as well as information confirming location of users and that may indicate the possible occurrence of undesired events, including natural disasters or acts of terror, for instance. RMS feeds may also be monitored. Confirmation of location may include a user declaration, a mobile phone or computing device application that obtains authorization from a user to post or share the user's location (e.g., through Places® of the Facebook application; or through another application that the user allows to access a location of a mobile device), and triangulation, where authorized. IP addressing or trace routes may also be performed to determine user locations. The tools may further include Web searching tools, including a Web crawler that culls information off Internet Web pages. Whether or not locations retrieved from these (or many other) sources of information are GPS-based, the system may convert the locations to GPS locations and seek to correlate such GPS locations with corresponding locations on or near the intelligent utility grid system. With such correlation, the system disclosed herein may be able to characterize an undesired event as malicious or not (e.g., a security risk or an engineering risk).

Upon receipt of the data 2000 by the CEP bus 147, the data 2000 may be pre-processed by a filter module 2002. The filter module 2002 may be responsible to pre-process the data 2000 to determine the relevant data of that included in the data 2000. For example, the CEP bus 147 may receive power outage information for other Regions/Countries and Twitter trending statistics. Filtered (or pre-processed) data 2003 may then be received by a rules engine module 2004. The rules engine module 2004 may include various predetermined rules or criteria upon which to assess the pre-processed data to determine if the pre-processed data is representative of one or more occurrences of an undesired event associated with residual risk. These rules may be used to identify conditions present in INDE that may indicate that an undesired event is occurring or may occur that is due to malicious activity. For example, a power outage in a particular location could indicate that a malicious attack on INDE was occurring or that the power outage is due to non-malicious causes, such as adverse weather conditions or vehicular accidents. The rules engine module 2004 may receive information from a centralized control center indicating that the outage has occurred. Alternatively, the determination of outages or other undesired events may be made through sensor outputs, meter data, or other suitable manners of detection.

In one embodiment, the rules engine module 2004 may receive the state of a particular device or conditions as conveyed by the data 2000 (e.g., meter outage). Through determination of the state of various conditions, the rules engine module 2004 may apply predetermined rules to the various determined states to determine whether a malicious cause is likely responsible for the occurrence of undesired events. The rules engine module 2004 may generate a probability level to convey the likelihood that occurrence of an undesired event is due to malicious activity. The rules engine module 104 may generate risk probability as having a particular qualitative probability level “high,” “medium,” or “low” depending on the determination made by the rules engine. For example, the rules engine module 104 may use a predetermined rule looking at an outage location and weather conditions based on information received in the data 2000. In alternative examples, the probability level may be expressed using fewer or additional qualitative levels, a percentage, or other suitable expression. The rules engine module 2004 may determine that if an outage is present in a particular location (e.g., zip code 12345) and current weather information indicates that a tornado is present in the zip code 12345, then there is a low probability that such outage is due to malicious intent and instead is likely due to other causes, such as inclement weather. In such a case, the rules engine module 2004 may determine that the probability of such occurrence being malicious is “low” according to predetermined levels. The threshold at which each level is determined may be based on various criteria, such as temperature, location, traffic accidents nearby, fires nearby, reports of gun shots nearby, dollars, a number of Twitter statistics or statistics related to any sort of social networking posts or communications, PMU values, recent storms, Homeland Security Threat Levels, Department of Defense Threat Levels, National Threat Levels, time of day, and the like.

Table 3 provides a number of other examples indicating a list of non-exhaustive examples illustrating how rules implemented by the rules engine module may be used to indicate the probability that a malicious attack on the INDE system may be occurring.

TABLE 3 Example Rules Rule #2: IF (zip code = 12345) AND (power outage = TRUE) AND (Vehicle Traffic Feed = ‘Car collision with electrical pole’) THEN NOT ‘Denial of Service Attack’ This shows that a possible reason why power went out in a neighborhood is because a vehicle hit an electric pole. Rule #3: IF (zip code = 12345) AND ((TEST MSG = ‘Power Out’) > 25) AND (Device Status = ‘Unknown’) AND (Connectivity State = ‘Disconnected’) and (Constituent Feeds = ‘Yellow Alert’) THEN ‘Remote Attack’ This shows that a remote attack taking place that is being reported on by at least 25 people in the same GPS coordinates/zipcode using text messaging. Rule #4: IF (zip code = 12345) AND (Time = 11:01:30) AND (Device Status.IPAddress_1.Power = FALSE) AND (Device Status.IPAddress_2.Power = FALSE) AND (Device Status.IPAddress_1.Accelerometer = TRUE) AND (Device Status.IPAddress_2. Accelerometer = TRUE) AND (Time = 11:16:30) AND (Device Status.IPAddress_1.Power = TRUE) AND (Device Status.IPAddress_2.Power = TRUE) AND (Device Status.IPAddress_1.Accelerometer = TRUE) AND (Device Status.IPAddress_2. Accelerometer = TRUE) THEN ‘Remote Tampering’ This shows that a meter device was off at 11:01 but 15 minutes later at 11:16 it was on but the accelerometer did not detect movement. As a result a remote tampering attack is suspected. Rule #5: IF (zip code = 12345) AND (Police Report.Shooting = True) AND (Collector Status = FALSE) THEN ‘Collector Physically Damaged’ This shows that the collector possibly was damaged by a stray (or intentional) bullet.

Upon determination of the probability level that a particular undesired event is being maliciously caused, the rules engine module 2004 may cross-reference the undesired event with a historical correlation module 2006. The historical correlation module 2006 may reference the data 2000 and determine if any such conditions have previously indicated occurrence of a malicious attack or have indicated a false alarm in such a manner. The historical correlation module 2006 may retrieve historical data from various sources, such as the event logs 135, historical data 136, and operational data 137 (see FIG. 1), for example, to obtain the relevant historical data. For example, in the scenario given above in which an outage is present in a particular area (e.g., zip code 12345) and current weather information indicates that a tornado is present in the zip code 12345, the historical correlation module 2006 may retrieve historical information regarding the zip code 12345 that indicates that no tornado has ever been recorded in the area or that the area is not generally known for tornado occurrences. If such historical information was retrieved, the rules engine module 104 may determine that the state indicating that a tornado is likely an improbable occurrence and would determine that the probability of malicious activity is “high.” However, if the historical information indicated that tornados or conditions conducive to tornados had historically been present, then the earlier indication of a “low” probability would be maintained by the rules engine module 2004.

Upon determination that a malicious attack is likely to be occurring or has occurred, indication of such may be provided by the rules engine module 2004 via the CPE bus 147 to the SIEM 162 represented in FIG. 20 as a residual risk event message 2008. The residual risk event message 2008 may include the condition analyzed, such as an outage, as well as the determination of the probability level of malicious causes of the condition and rely on non-IT data for determination thereof. The SIEM 162 may also receive information technology (IT) events (or activity) 2010 from IT hardware or software such as routers, switches, firewalls, intrusion detection systems, or virus protection. The IT events 2010 may provide various information regarding the IT infrastructure of INDE, such as that included in switch logs. The IT events 2010 may include information such as device logs, IDS/IPS alerts, WIDS/WIPS alerts, antivirus alerts, and device configurations, for example. Any data or information not falling within these general types of IT events are considered non-IT data as that term is referred to herein. Upon receipt of the residual risk event message 2008 and the IT event 2010, the SIEM 162 may determine a risk characterization that indicates the type of risk caused by the malicious activity. Upon determination of the risk characterization, the SIEM 162 may generate a risk characterization message 2012 that may be provided to a system administrator or other individual/entity responsible for reacting to any occurrences of undesired events. The risk characterization message 2012 may indicate not only the probability level of malicious intent of a particular event, but also determine the area or areas at risk, such as security, engineering, communications, etc. In one example, this may be performed by the risk being characterized as being the result of either an unintentional (or unexpected) hazard or a malicious attack. The correlation of the residual risk with the IT/network information facilitates the assignment of areas at risk.

Referring back to FIG. 1, one or more of the systems in the Operations Control Center 116 that are outside of the INDE Core 120 are legacy product systems that a utility may have and that may provide non-IT data. Examples of these legacy product systems include the Operations SOA Support 148, Geographic Information System 149, Work Management System 150, Call Management 151, Circuit & Load Flow Analysis, Planning, Lightning Analysis and Grid Simulation Tools 152, Meter Data Collection Head End(s) 153, Demand Response Management System 154, Outage Management System 155, Energy Management System 156, Distribution Management System 157, IP Network Services 158, and Dispatch Mobile Data System 159. However, these legacy product systems may not be able to process or handle data that is received from a smart grid. The INDE Core 120 may be able to receive the data from the smart grid, process the data from the smart grid, and transfer the processed data to the one or more legacy product systems in a fashion that the legacy product systems may use (such as particular formatting particular to the legacy product system). In this way, the INDE Core 120 may be viewed as a middleware.

The operations control center 116, including the INDE CORE 120, may communicate with the Enterprise IT 115. Generally speaking, the functionality in the Enterprise IT 115 comprises back-office operations. Specifically, the Enterprise IT 115 may use the enterprise integration environment bus 114 to send data to various systems within the Enterprise IT 115, including Business Data Warehouse 104, Business Intelligence Applications 105, Enterprise Resource Planning 106, various Financial Systems 107, Customer Information System 108, Human Resource System 109, Asset Management System 110, Enterprise SOA Support 111, Network Management System 112, and Enterprise Messaging Services 113. The Enterprise IT 115 may further include a portal 103 to communicate with the Internet 101 via a firewall 102.

INDE SUBSTATION

FIG. 4 illustrates an example of the high level architecture for the INDE SUBSTATION 180 group. This group may include elements that are actually hosted in the substation 170 at a substation control house on one or more servers co-located with the substation electronics and systems.

Table 4 below lists and describes certain INDE SUBSTATION 180 group elements. Data security services 171 may be a part of the substation environment; alternatively, they may be integrated into the INDE SUBSTATION 180 group.

TABLE 4 INDE SUBSTATION Elements INDE SUBSTATION ELEMENTS Description Non-Operational Performance and health data; this is a distributed Data Store 181 data historian component Operational Data Real time grid state data; this is part of a true Store 182 distributed database Interface/ Support for communications, including TCP/IP, Communications SNMP, DHCP, SFTP, IGMP, ICMP, DNP3, IEC Stack 187 61850, etc. Distributed/remote Support for remote program distribution, inter- computing support process communication, etc. (DCE, JINI, OSGi, for 186 example) Signal/Waveform Support for real time digital signal processing Processing 185 components; data normalization; engineering units conversions Detection/ Support for real time event stream processing, Classification detectors and event/waveform classifiers (ESP, Processing 184 ANN, SVM, etc.) Substation Support for programmable real time analytics Analytics 183 applications; DNP3 scan master; The substation analytics may allow for analysis of the real-time operational and non-operational data in order to determine if an “event” has occurred. The “event” determination may be rule-based with the rules determining whether one of a plurality of possible events occurring based on the data. The substation analytics may also allow for automatic modification of the operation of the substation based on a determined event. In this way, the grid (including various portions of the grid) may be “self-healing.” This “self-healing” aspect avoids the requirement that the data be transmitted to a central authority, the data be analyzed at the central authority, and a command be sent from the central authority to the grid before the problem in the grid be corrected. In addition to the determination of the “event,” the substation analytics may also generate a work-order for transmission to a central authority. The work- order may be used, for example, for scheduling a repair of a device, such as a substation. Substation LAN 172 Local networking inside the substation to various portions of the substation, such as microprocessor relays 173, substation instrumentation 174, event file recorders 175, and station RTUs 176. Security services The substation may communicate externally with 171 various utility communications networks via the security services layer.

As discussed above, different elements within the smart grid may include additional functionality including additional processing/analytical capability and database resources. The use of this additional functionality within various elements in the smart grid enables distributed architectures with centralized management and administration of applications and network performance. For functional, performance, and scalability reasons, a smart grid involving thousands to tens of thousands of INDE SUBSTATIONS 180 and tens of thousands to millions of grid devices may include distributed processing, data management, and process communications.

The INDE SUBSTATION 180 may include one or more processors and one or more memory devices (such as substation non-operational data 181 and substation operations data 182). Non-operational data 181 and substation operations data 182 may be associated with and proximate to the substation, such as located in or on the INDE SUBSTATION 180. The INDE SUBSTATION 180 may further include components of the smart grid that are responsible for the observability of the smart grid at a substation level. The INDE SUBSTATION 180 components may provide three primary functions: operational data acquisition and storage in the distributed operational data store; acquisition of non-operational data and storage in the historian; and local analytics processing on a real time (such as a sub-second) basis. Processing may include digital signal processing of voltage and current waveforms, detection and classification processing, including event stream processing and communications of processing results to local systems and devices as well as to systems at the operations control center 116. Communication between the INDE SUBSTATION 180 and other devices in the grid may be wired, wireless, or a combination of wired and wireless. For example, the transmission of data from the INDE SUBSTATION 180 to the operations control center 116 may be wired. The INDE SUBSTATION 180 may transmit data, such as operation/non-operational data or event data, to the operations control center 116. Routing device 190 may route the transmitted data to one of the operational/non-operational data bus 146 or the event bus 147.

Demand response optimization for distribution loss management may also be performed here. This architecture is in accordance with the distributed application architecture principle previously discussed.

For example, connectivity data may be duplicated at the substation 170 and at the operations control center 116, thereby allowing a substation 170 to operate independently even if the data communication network to the operations control center 116 is not functional. With this information (connectivity) stored locally, substation analytics may be performed locally even if the communication link to the operations control center is inoperative.

Similarly, operational data may be duplicated at the operations control center 116 and at the substations 170. Data from the sensors and devices associated with a particular substation may be collected and the latest measurement may be stored in this data store at the substation. The data structures of the operational data store may be the same and hence database links may be used to provide seamless access to data that resides on the substations thru the instance of the operational data store at the control center. This provides a number of advantages including alleviating data replication and enabling substation data analytics, which is more time sensitive, to occur locally and without reliance on communication availability beyond the substation. Data analytics at the operations control center level 116 may be less time sensitive (as the operations control center 116 may typically examine historical data to discern patterns that are more predictive, rather than reactive) and may be able to work around network issues, if any.

Finally, historical data may be stored locally at the substation and a copy of the data may be stored at the control center. Or, database links may be configured on the repository instance at the operations control center 116, providing the operations control center access to the data at the individual substations. Substation analytics may be performed locally at the substation 170 using the local data store. Specifically, using the additional intelligence and storage capability at the substation enables the substation to analyze itself and to correct itself without input from a central authority. Alternatively, historical/collective analytics may also be performed at the operations control center level 116 by accessing data at the local substation instances using the database links.

INDE DEVICE

The INDE DEVICE 188 group may comprise any variety of devices within the smart grid, including various sensors within the smart grid, such as various distribution grid devices 189 (e.g., line sensors on the power lines), meters 163 at the customer premises, etc. The INDE DEVICE 188 group may comprise a device added to the grid with particular functionality (such as a smart Remote Terminal Unit (RTU) that includes dedicated programming), or may comprise an existing device within the grid with added functionality (such as an existing open architecture pole top RTU that is already in place in the grid that may be programmed to create a smart line sensor or smart grid device). The INDE DEVICE 188 may further include one or more processors and one or more memory devices.

Existing grid devices may not be open from the software standpoint, and may not be capable of supporting much in the way of modern networking or software services. The existing grid devices may have been designed to acquire and store data for occasional offload to some other device such as a laptop computer, or to transfer batch files via PSTN line to a remote host on demand. These devices may not be designed for operation in a real time digital network environment. In these cases, the grid device data may be obtained at the substation level 170, or at the operations control center level 116, depending on how the existing communications network has been designed. In the case of metered networks, it will normally be the case that data is obtained from the meter data collection engine, since meter networks are usually closed and the meters may not be addressed directly. As these networks evolve, meters and other grid devices may be individually addressable, so that data may be transported directly to where it is needed, which may not necessarily be the operations control center 116, but may be anywhere on the grid.

Devices such as faulted circuit indicators may be married with wireless network interface cards, for connection over modest speed (such as 100 kbps) wireless networks. These devices may report status by exception and carry out fixed pre-programmed functions. The intelligence of many grid devices may be increased by using local smart RTUs. Instead of having poletop RTUs that are designed as fixed function, closed architecture devices, RTUs may be used as open architecture devices that can be programmed by third parties and that may serve as an INDE DEVICE 188 in the INDE Reference Architecture. Also, meters at customers' premises may be used as sensors. For example, meters may measure consumption (such as how much energy is consumed for purposes of billing) and may measure voltage (for use in volt/VAR optimization).

FIG. 5 illustrates an example architecture for INDE DEVICE 188 group. Table 5 describes the certain INDE DEVICE 188 elements. The smart grid device may include an embedded processor, so the processing elements are less like SOA services and more like real time program library routines, since the DEVICE group is implemented on a dedicated real time DSP or microprocessor.

TABLE 5 INDE DEVICE Elements INDE DEVICE ELEMENTS Description Ring buffers 502 Local circular buffer storage for digital waveforms sampled from analog transducers (voltage and current waveforms for example) which may be used to hold the data for waveforms at different time periods so that if an event is detected, the waveform data leading up to the event may also be stored. Device status buffers 504 Buffer storage for external device state and state transition data Three phase frequency tracker 506 Computes a running estimate of the power frequency from all three phases; used for frequency correction to other data as well as in grid stability and power quality measures (especially as relates to DG) Fourier transform block 508 Conversion of time domain waveforms to frequency domain to enable frequency domain analytics Time domain signal analytics Processing of the signals in the time domain; 510 extraction of transient and envelop behavior measures Frequency domain signal Processing of the signals in the frequency domain; analytics 512 extraction of RMS and power parameters Secondary signal analytics 514 Calculation and compensation of phasors; calculation of selected error/fault measures Tertiary signal analytics 516 Calculation of synchrophasors based on GPS timing and a system reference angle Event analysis and triggers 518 Processing of all analytics for event detection and triggering of file capture. Different types of INDE DEVICES may include different event analytical capability. For example, a line sensor may examine ITIC events, examining spikes in the waveform. If a spike occurs (or a series of spikes occur), the line sensor, with the event analytical capability, may determine that an “event” has occurred and also may provide a recommendation as to the cause of the event. The event analytical capability may be rule-based, with different rules being used for different INDE DEVICES and different applications. File storage - Capture of data from the ring buffers based on capture/formatting/transmission event triggers 520 Waveform streaming service Support for streaming of waveforms to a remote 522 display client Communications stack Support for network communications and remote program load GPS Timing 524 Provides high resolution timing to coordinate applications and synchronize data collection across a wide geographic area. The generated data may include a GPS data frame time stamp 526. Status analytics 528 Capture of data for status messages

FIG. 1 further depicts customer premises 179, which may include one or more Smart Meters 163, an in-home display 165, one or more sensors 166, and one or more controls 167. In practice, sensors 166 may register data at one or more devices at the customer premises 179. For example, a sensor 166 may register data at various major appliances within the customer premises 179, such as the furnace, hot water heater, air conditioner, etc. The data from the one or more sensors 166 may be sent to the Smart Meter 163, which may package the data for transmission to the operations control center 116 via utility communication network 160. The in-home display 165 may provide the customer at the customer premises with an output device to view, in real-time, data collected from Smart Meter 163 and the one or more sensors 166. In addition, an input device (such as a keyboard) may be associated with in-home display 165 so that the customer may communicate with the operations control center 116. In one embodiment, the in-home display 165 may comprise a computer resident at the customer premises.

The customer premises 165 may further include controls 167 that may control one or more devices at the customer premises 179. Various appliances at the customer premises 179 may be controlled, such as the heater, air conditioner, etc., depending on commands from the operations control center 116.

As depicted in FIG. 1, the customer premises 169 may communicate in a variety of ways, such as via the Internet 168, the public-switched telephone network (PSTN) 169, or via a dedicated line (such as via collector 164). Via any of the listed communication channels, the data from one or more customer premises 179 may be sent. As shown in FIG. 1, one or more customer premises 179 may comprise a Smart Meter Network 178 (comprising a plurality of smart meters 163), sending data to a collector 164 for transmission to the operations control center 116 via the utility management network 160. Further, various sources of distributed energy generation/storage 162 (such as solar panels, etc.) may send data to a monitor control 161 for communication with the operations control center 116 via the utility management network 160.

As discussed above, the devices in the power grid outside of the operations control center 116 may include processing and/or storage capability. The devices may include the INDE SUBSTATION 180 and the INDE DEVICE 188. In addition to the individual devices in the power grid including additional intelligence, the individual devices may communicate with other devices in the power grid, in order to exchange information (including sensor data and/or analytical data (such as event data)) in order to analyze the state of the power grid (such as determining faults) and in order to change the state of the power grid (such as correcting for the faults). Specifically, the individual devices may use the following: (1) intelligence (such as processing capability); (2) storage (such as the distributed storage discussed above); and (3) communication (such as the use of the one or more buses discussed above). In this way, the individual devices in the power grid may communicate and cooperate with one another without oversight from the operations control center 116.

For example, the INDE architecture disclosed above may include a device that senses at least one parameter on the feeder circuit. The device may further include a processor that monitors the sensed parameter on the feeder circuit and that analyzes the sensed parameter to determine the state of the feeder circuit. For example, the analysis of the sense parameter may comprise a comparison of the sensed parameter with a predetermined threshold and/or may comprise a trend analysis. One such sensed parameter may include sensing the waveforms and one such analysis may comprise determining whether the sensed waveforms indicate a fault on the feeder circuit. The device may further communicate with one or more substations. For example, a particular substation may supply power to a particular feeder circuit. The device may sense the state of the particular feeder circuit, and determine whether there is a fault on the particular feeder circuit. The device may communicate with the substation. The substation may analyze the fault determined by the device and may take corrective action depending on the fault (such as reducing the power supplied to the feeder circuit). In the example of the device sending data indicating a fault (based on analysis of waveforms), the substation may alter the power supplied to the feeder circuit without input from the operations control center 116. Or, the substation may combine the data indicating the fault with information from other sensors to further refine the analysis of the fault. The substation may further communicate with the operations control center 116, such as the outage intelligence application (such as discussed FIG. 13) and/or the fault intelligence application (such as discussed in FIG. 14). Thus, the operations control center 116 may determine the fault and may determine the extent of the outage (such as the number of homes affected by the fault). In this way, the device sensing the state of the feeder circuit may cooperatively work with the substation in order to correct a potential fault with or without requiring the operations control center 116 to intervene.

As another example, a line sensor, which includes additional intelligence using processing and/or memory capability, may produce grid state data in a portion of the grid (such as a feeder circuit). The grid state data may be shared with the demand response management system 155 at the operations control center 116. The demand response management system 155 may control one or more devices at customer sites on the feeder circuit in response to the grid state data from the line sensor. In particular, the demand response management system 155 may command the energy management system 156 and/or the distribution management system 157 to reduce load on the feeder circuit by turning off appliances at the customer sites that receive power from the feeder circuit in response to line sensor indicating an outage on the feeder circuit. In this way, the line sensor in combination with the demand response management system 155 may automatically shift load from a faulty feeder circuit and then isolate the fault.

As still another example, one or more relays in the power grid may have a microprocessor associated with it. These relays may communicate with other devices and/or databases resident in the power grid in order to determine a fault and/or control the power grid.

INDS Concept and Architecture

Outsourced Smart Grid Data/Analytics Services Model

One application for the smart grid architecture allows the utility to subscribe to grid data management and analytics services while maintaining traditional control systems and related operational systems in-house. In this model, the utility may install and own grid sensors and devices (as described above), and may either own and operate the grid data transport communication system, or may outsource it. The grid data may flow from the utility to a remote Intelligent Network Data Services (INDS) hosting site, where the data may be managed, stored, and analyzed. The utility may then subscribe to data and analytics services under an appropriate services financial model. The utility may avoid the initial capital expenditure investment and the ongoing costs of management, support, and upgrade of the smart grid data/analytics infrastructure, in exchange for fees. The INDE Reference Architecture, described above, lends itself to the outsourcing arrangement described herein.

INDS Architecture for Smart Grid Services

In order to implement the INDS services model, the INDE Reference Architecture may be partitioned into a group of elements that may be hosted remotely, and those that may remain at the utility. FIG. 6 illustrates how the utility architecture may look once the INDE CORE 120 has been made remote. A server may be included as part of the INDE CORE 120 that may act as the interface to the remote systems. To the utility systems, this may appear as a virtual INDE CORE 602.

As the overall block diagram 600 in FIG. 6 shows, the INDE SUBSTATION 180 and INDE DEVICE 188 groups are unchanged from that depicted in FIG. 1. The multiple bus structure may also still be employed at the utility as well.

The INDE CORE 120 may be remotely hosted, as the block diagram 700 in FIG. 7 illustrates. At the hosting site, INDE COREs 120 may be installed as needed to support utility INDS subscribers (shown as North American INDS Hosting Center 702). Each CORE 120 may be a modular system, so that adding a new subscriber is a routine operation. A party separate from the electric utility may manage and support the software for one, some, or all of the INDE COREs 120, as well as the applications that are downloaded from the INDS hosting site to each utility's INDE SUBSTATION 180 and INDE DEVICES 188.

In order to facilitate communications, high bandwidth low latency communications services, such as via network 704 (e.g., a Multi-Protocol Label Switching (MPLS) or other WAN), may be used that can reach the subscriber utility operations centers, as well as the INDS hosting sites. As shown in FIG. 7, various areas may be served, such as California, Florida, and Ohio. This modularity of the operations not only allows for efficient management of various different grids, it also allows for better inter-grid management. There are instances where a failure in one grid may affect operations in a neighboring grid. For example, a failure in the Ohio grid may have a cascade effect on operations in a neighboring grid, such as the mid-Atlantic grid. Using the modular structure as illustrated in FIG. 7 allows for management of the individual grids and management of inter-grid operations. Specifically, an overall INDS system (which includes a processor and a memory) may manage the interaction between the various INDE COREs 120. This may reduce the possibility of a catastrophic failure that cascades from one grid to another. For example, a failure in the Ohio grid may cascade to a neighboring grid, such as the mid-Atlantic grid. The INDE CORE 120 dedicated to managing the Ohio grid may attempt to correct for the failure in the Ohio grid. And, the overall INDS system may attempt to reduce the possibility of a cascade failure occurring in neighboring grids.

Specific Examples of Functionality in INDE CORE

As shown in FIGS. 1, 6, and 7, various functionalities (represented by blocks) are included in the INDE CORE 120, two of which are meter data management services (MDMS) 121 and metering analytics and services 122. Because of the modularity of the architecture, various functionality, such as MDMS 121 and metering analytics and services 122, may be incorporated.

Observability Processes

As discussed above, one functionality of the application services may include observability processes. The observability processes may allow the utility to “observe” the grid. These processes may be responsible for interpreting the raw data received from all the sensors and devices on the grid and turning them into actionable information. FIG. 8 includes a listing of some examples of the observability processes.

FIG. 9 illustrates a flow diagram 900 of the Grid State Measurement & Operations Processes. The Data Scanner may request meter data, at block 902. The request may be sent to one or more grid devices, substation computers, and line sensor RTUs. In response to the request, the devices may collect operations data, as shown at blocks 904, 908, 912, and may send data (such as one, some or all of the operational data, such as Voltage, Current, Real Power, and Reactive Power data), as shown at blocks 906, 910, 914. The data scanner may collect the operational data, as shown at block 926, and may send the data to the operational data store, as shown at block 934. The operational data store may store the operational data, as shown at block 938. The operational data store may further send a snapshot of the data to the historian, as shown at block 940, and the historian may store the snapshot of the data, as shown at block 942.

The meter state application may send a request for meter data to the Meter DCE, as shown in block 924, which in turn sends a request to one or more meters to collect meter data, as shown at block 920. In response to the request, the one or more meters collects meter data, as shown at block 916, and sends the voltage data to the Meter DCE, as shown at block 918. The Meter DCE may collect the voltage data, as shown at block 922, and send the data to the requestor of the data, as shown at block 928. The meter state application may receive the meter data, as shown at block 930, and determine whether it is for a single value process or a voltage profile grid state, as shown at block 932. If it is for the single value process, the meter data is sent to the requesting process, as shown at block 936. If the meter data is for storage to determine the grid state at a future time, the meter data is stored in the operational data store, as shown at block 938. The operational data store further sends a snapshot of the data to the historian, as shown at block 940, and the historian stores the snapshot of the data, as shown at block 942.

FIG. 9 further illustrates actions relating to demand response (DR). Demand response refers to dynamic demand mechanisms to manage customer consumption of electricity in response to supply conditions, for example, having electricity customers reduce their consumption at critical times or in response to market prices. This may involve actually curtailing power used or by starting on site generation which may or may not be connected in parallel with the grid. This may be different from energy efficiency, which means using less power to perform the same tasks, on a continuous basis or whenever that task is performed. In demand response, customers, using one or more control systems, may shed loads in response to a request by a utility or market price conditions. Services (lights, machines, air conditioning) may be reduced according to a preplanned load prioritization scheme during the critical timeframes. An alternative to load shedding is on-site generation of electricity to supplement the power grid. Under conditions of tight electricity supply, demand response may significantly reduce the peak price and, in general, electricity price volatility.

Demand response may generally be used to refer to mechanisms used to encourage consumers to reduce demand, thereby reducing the peak demand for electricity. Since electrical systems are generally sized to correspond to peak demand (plus margin for error and unforeseen events), lowering peak demand may reduce overall plant and capital cost requirements. Depending on the configuration of generation capacity, however, demand response may also be used to increase demand (load) at times of high production and low demand. Some systems may thereby encourage energy storage to arbitrage between periods of low and high demand (or low and high prices). As the proportion of intermittent power sources such as wind power in a system grows, demand response may become increasingly important to effective management of the electric grid.

The DR state application may request the DR available capacity, as shown at block 954. The DR management system may then request available capacity from one or more DR home devices, as shown at block 948. The one or more home devices may collect available DR capacity in response to the request, as shown at block 944, and send the DR capacity and response data to the DR management system, as shown at block 946. The DR management system may collect the DR capacity and response data, as shown at block 950, and send the DR capacity and response data to the DR state application, as shown at block 952. The DR state application may receive the DR capacity and response data, as shown at block 956, and send the capacity and response data to the operational data store, as shown at block 958. The operational data store may store the DR capacity and response data data, as shown at block 938. The operational data store may further send a snapshot of the data to the historian, as shown at block 940, and the historian may store the snapshot of the data, as shown at block 942.

The substation computer may request application data from the substation application, as shown at block 974. In response, the substation application may request application from the substation device, as shown at block 964. The substation device may collect the application data, as shown at block 960, and send the application data to the substation device (which may include one, some or all of Voltage, Current, Real Power, and Reactive Power data), as shown at block 962. The substation application may collect the application data, as shown at block 966, and send the application data to the requestor (which may be the substation computer), as shown at block 968. The substation computer may receive the application data, as shown at block 970, and send the application data to the operational data store, as shown at block 972.

The grid state measurement and operational data process may comprise deriving the grid state and grid topology at a given point in time, as well as providing this information to other system and data stores. The sub-processes may include: (1) measuring and capturing grid state information (this relates to the operational data pertaining to the grid that was discussed previously); (2) sending grid state information to other analytics applications (this enables other applications, such as analytical applications, access to the grid state data); (3) persisting grid state snapshot to connectivity/operational data store (this allows for updating the grid state information to the connectivity/operational data store in the appropriate format as well as forwarding this information to the historian for persistence so that a point-in-time grid topology may be derived at a later point in time); (4) deriving grid topology at a point in time based on default connectivity and current grid state (this provides the grid topology at a given point in time by applying the point-in-time snapshot of the grid state in the historian to the base connectivity in the connectivity data store, as discussed in more detail below); and (5) providing grid topology information to applications upon request.

With regard to sub-process (4), the grid topology may be derived for a predetermined time, such as in real-time, 30 seconds ago, 1 month ago, etc. In order to recreate the grid topology, multiple databases may be used, and a program to access the data in the multiple databases to recreate the grid topology. One database may comprise a relational database that stores the base connectivity data (the “connectivity database”). The connectivity database may hold the grid topology information as built in order to determine the baseline connectivity model. Asset and topology information may be updated into this database on a periodic basis, depending on upgrades to the power grid, such as the addition or modification of circuits in the power grid (e.g., additional feeder circuits that are added to the power grid). The connectivity database may be considered “static” in that it does not change. The connectivity database may change if there are changes to the structure of the power grid. For example, if there is a modification to the feeder circuits, such as an addition of a feeder circuit, the connectivity database may change.

One example of the structure 1800 of the connectivity database may be derived from the hierarchical model depicted in FIGS. 18A-D. The structure 1800 is divided into four sections, with FIG. 18A being the upper-left section, FIG. 18B being the upper-right section, FIG. 18C being the bottom-left section, and FIG. 18D being the bottom-right section. Specifically, FIGS. 18A-D are an example of an entity relationship diagram, which is an abstract method to represent the baseline connectivity database. The hierarchical model in FIGS. 18A-D may hold the meta-data that describes the power grid and may describe the various components of a grid and the relationship between the components.

A second database may be used to store the “dynamic” data. The second database may include a non-relational database. One example of a non-relational database may include a historian database, which stores the time series non-operational data as well as the historical operational data. The historian database may store a series of “flat” records such as: (1) time stamp; (2) device ID; (3) a data value; and (4) a device status. Furthermore, the stored data may be compressed. Because of this, the operational/non-operational data in the power grid may be stored easily, and may be manageable even though a considerable amount of data may be available. For example, data on the order of 5 Terabytes may be online at any given time for use in order to recreate the grid topology. Because the data is stored in the simple flat record (such as no organizational approach), it allows efficiency in storing data. As discussed in more detail below, the data may be accessed by a specific tag, such as the time stamp.

Various analytics for the grid may wish to receive, as input, the grid topology at a particular point in time. For example, analytics relating to power quality, reliability, asset health, etc., may use the grid topology as input. In order to determine the grid topology, the baseline connectivity model, as defined by the data in the connectivity database, may be accessed. For example, if the topology of a particular feeder circuit is desired, the baseline connectivity model may define the various switches in the particular feeder circuit in the power grid. After which, the historian database may be accessed (based on the particular time) in order to determine the values of the switches in the particular feeder circuit. Then, a program may combine the data from the baseline connectivity model and the historian database in order to generate a representation of the particular feeder circuit at the particular time.

A more complicated example to determine the grid topology may include multiple feeder circuits (e.g., feeder circuit A and feeder circuit B) that have an inter-tie switch and sectionalizing switches. Depending on the switch states of certain switches (such as the inter-tie switch and/or the sectionalizing switches), sections of the feeder circuits may belong to feeder circuit A or feeder circuit B. The program that determines the grid topology may access the data from both the baseline connectivity model and the historian database in order to determine the connectivity at a particular time (e.g., which circuits belong to feeder circuit A or feeder circuit B).

FIG. 10 illustrates a flow diagram 1000 of the Non-Operational Data processes. The non-operational extract application may request non-operational data, as shown at block 1002. In response, a data scanner may gather non-operational data, as shown at block 1004, where by various devices in the power grid, such as grid devices, substation computers, and line sensor RTUs, may collect non-operational data, as shown at blocks 1006, 1008, 1110. As discussed above, non-operational data may include temperature, power quality, etc. The various devices in the power grid, such as grid devices, substation computers, and line sensor RTUs, may send the non-operational data to the data scanner, as shown at blocks 1012, 1014, 1116. The data scanner may collect the non-operational data, as shown at block 1018, and send the non-operational data to the non-operational extract application, as shown at block 1020. The non-operational extract application may collect the non-operational data, as shown at block 1022, and send the collected non-operational data to the historian, as shown at block 1024. The historian may receive the non-operational data, as shown at block 1026, store the non-operational data, as shown at block 1028, and send the non-operational data to one or more analytics applications, as shown at block 1030.

FIG. 11 illustrates a flow diagram 1100 of the Event Management processes. Data may be generated from various devices based on various events in the power grid and sent via the event bus 147. For example, the meter data collection engine may send power outage/restoration notification information to the event bus, as shown at block 1102. The line sensors RTUs generate a fault message, and may send the fault message to the event bus, as shown at block 1104. The substation may analytics may generate a fault and/or outage message, and may send the fault and/or outage message to the event bus, as shown at block 1106. The historian may send signal behavior to the event bus, as shown at block 1108. And, various processes may send data via the event bus 147. For example, the fault intelligence process, discussed in more detail in FIG. 14, may send a fault analysis event via the event bus, as shown at block 1110. The outage intelligence process, discussed in more detail in FIG. 13, may send an outage event via the event bus, as shown at block 1112. The event bus may collect the various events, as shown at block 1114. And, the Complex Event Processing (CEP) services may process the events sent via the event bus, as shown at block 1120. The CEP services may process queries against multiple concurrent high speed real time event message streams. After processing by the CEP services, the event data may be sent via the event bus, as shown at block 1118. The historian may receive via the event bus one or more event logs for storage, as shown at block 1116. Also, the event data may be received by one or more applications, such as the outage management system (OMS), outage intelligence, fault analytics, etc., as shown at block 1122. In this way, the event bus may send the event data to an application, thereby avoiding the “silo” problem of not making the data available to other devices or other applications.

FIG. 12 illustrates a flow diagram 1200 of the Demand Response (DR) Signaling processes. DR may be requested by the distribution operation application, as shown at block 1244. In response, the grid state/connectivity may collect DR availability data, as shown at block 1202, and may send the data, as shown at block 1204. the distribution operation application may distribute the DR availability optimization, as show at block 1246, via the event bus (block 1254), to one or more DR Management Systems. The DR Management System may send DR information and signals to one or more customer premises, as shown at block 1272. The one or more customer premises may receive the DR signals, as shown at block 1266, and send the DR response, as shown at block 1268. The DR Management may receive the DR response, as shown at block 1274, and send DR responses to one, some or all of the operations data bus 146, the billing database, and the marketing database, as shown at block 1276. The billing database and the marketing database may receive the responses, as shown at blocks 1284, 1288. The operations data bus 146 may also receive the responses, as shown at block 1226, and send the DR responses and available capacity to the DR data collection, as shown at block 1228. The DR data collection may process the DR responses and available capacity, as shown at block 1291, and send the data to the operations data bus, as shown at block 1294. The operations data bus may receive the DR availability and response, as shown at block 1230, and send it to the grid state/connectivity. The grid state/connectivity may receive the data, as shown at block 1208. The received data may be used to determine the grid state data, which may be sent (block 1206) via the operations data bus (block 1220). The distribution operation application may receive the grid state data (as an event message for DR optimization), as shown at block 1248. Using the grid state data and the DR availability and response, the distribution operation application may run distribution optimization to generate distribution data, as shown at block 1250. The distribution data may be retrieved by the operations data bus, as shown at block 1222, and may be sent to the connectivity extract application, as shown at block 1240. The operational data bus may send data (block 1224) to the distribution operation application, which in turn may send one or more DR signals to one or more DR Management Systems (block 1252). The event bus may collect signals for each of the one or more DR Management Systems (block 1260) and send the DR signals to each of the DR Management Systems (block 1262). The DR Management System may then process the DR signals as discussed above.

The communication operation historian may send data to the event bus, as shown at block 1214. The communication operation historian may also send generation portfolio data, as shown at block 1212. Or, an asset management device such as a Ventyx® may request virtual power plant (VPP) information, as shown at block 1232. The operations data bus may collect the VPP data, as shown at block 1216, and send the data to the asset management device, as shown at block 1218. The asset management device may collect the VPP data, as shown at block 1234, run system optimization, as shown at block 1236, and send VPP signals to the event bus, as shown at block 1238. The event bus may receive the VPP signals, as shown at block 1256, and send the VPP signals to the distribution operation application, as shown at block 1258. The distribution operation application may then receive and process the event messages, as discussed above.

The connection extract application may extract new customer data, as shown at block 1278, to be sent to the Marketing Database, as shown at block 1290. The new customer data may be sent to the grid state/connectivity, as shown at block 1280, so that the grid state connectivity may receive new DR connectivity data, as shown at block 1210.

The operator may send one or more override signals when applicable, as shown at block 1242. The override signals may be sent to the distribution operation application. The override signal may be sent to the energy management system, as shown at block 1264, the billing database, as shown at block 1282, and/or the marketing database, as shown at block 1286.

FIG. 13 illustrates a flow diagram 1300 of the Outage Intelligence processes. Various devices and applications may send power outage notification, as shown at blocks 1302, 1306, 1310, 1314, 1318. The outage events may be collected by the event bus, as shown at block 1324, which may send the outage events to the complex event processing (CEP), as shown at block 1326. Further, various devices and applications may send power restoration status, as shown at blocks 1304, 1308, 1312, 1316, 1320. The CEP may receive outage and restoration status messages (block 1330), process the events (block 1332), and send event data (block 1334). The outage intelligence application may receive the event data (block 1335) and request grid state and connectivity data (block 1338). The operational data bus may receive the request for grid state and connectivity data (block 1344) and forward it to one or both of the operational data store and the historian. In response, the operational data store and the historian may send the grid state and connectivity data (blocks 1352, 1354) via the operational data bus (block 1346) to the outage intelligence application (block 1340). It is determined whether the grid state and connectivity data indicate whether this was a momentary, as shown at block 1342. If so, the momentaries are sent via the operational data bus (block 1348) to the momentaries database for storage (block 1350). If not, an outage case is created (block 1328) and the outage case data is stored and processed by the outage management system (block 1322).

The outage intelligence processes may: detect outages; classify & log momentaries; determine outage extent; determine outage root cause(s); track outage restoration; raise outage events; and update system performance indices.

FIG. 14 illustrates a flow diagram 1400 of the Fault Intelligence processes. The complex event processing may request data from one or more devices, as shown at block 1416. For example, the grid state and connectivity in response to the request may send grid state and connectivity data to the complex event processing, as shown at block 1404. Similarly, the historian in response to the request may send real time switch state to the complex event processing, as shown at block 1410. And, the complex event processing may receive the grid state, connectivity data, and the switch state, as shown at block 1418. The substation analytics may request fault data, as shown at block 1428. Fault data may be sent by a variety of devices, such as line sensor RTUs, and substation computers, as shown at blocks 1422, 1424. The various fault data, grid state, connectivity data, and switch state may be sent to the substation analytics for event detection and characterization, as shown at block 1430. The event bus may also receive event messages (block 1434) and send the event messages to the substation analytics (block 1436). The substation analytics may determine the type of event, as shown at block 1432. For protection and control modification events, the substation computers may receive a fault event message, as shown at block 1426. For all other types of events, the event may be received by the event bus (block 1438) and sent to the complex event processing (block 1440). The complex event processing may receive the event data (block 1420) for further processing. Similarly, the grid state and connectivity may send grid state data to the complex event processing, as shown at block 1406. And, the Common Information Model (CIM) warehouse may send meta data to the complex event processing, as shown at block 1414.

The complex event processing may send a fault event message, as shown at block 1420. The event bus may receive the message (block 1442) and send the event message to the fault intelligence application (block 1444). The fault intelligence application may receive the event data (block 1432) and request grid state, connectivity data, and switch state, as shown at block 1456. In response to the request, the fault intelligence application may send grid state and connectivity data (block 1408), and the historian may send switch state data (block 1412). The fault intelligence receives the data (block 1458), analyzes the data, and sends event data (block 1460). The event data may be received by the event bus (block 1446) and sent to the fault log file (block 1448). The fault log file may log the event data (block 1402). The event data may also be received by the operational data bus (block 1462) and send to one or more applications (block 1464). For example, the outage intelligence application may receive the event data (block 1466), discussed above with respect to FIG. 13. The work management system may also receive the event data in the form of a work order, as shown at block 1468. And, other requesting applications may receive the event data, as shown at block 1470.

The fault intelligent processes may be responsible for interpreting the grid data to derive information about current and potential faults within the grid. Specifically, faults may be detected using the fault intelligent processes. A fault is typically a short circuit caused when utility equipment fails or alternate path for current flow is created, for example, a downed power line. These processes may be used to detect typical faults (typically handled by the conventional fault detection and protection equipment—relays, fuses, etc.) as well as high-impedance faults within the grid that are not easily detectable using fault currents.

The fault intelligence process may also classify and categorize faults. This allows for faults to be classified and categorized. Currently, no standard exists for a systematic organization and classification for faults. A de-facto standard may be established for the same and implemented. The fault intelligence process may further characterize faults.

The fault intelligence may also determine fault location. Fault location in the distribution system may be a difficult task due to its high complexity and difficulty caused by unique characteristics of the distribution system such as unbalanced loading, three-, two-, and single-phase laterals, lack of sensors/measurements, different types of faults, different causes of short circuits, varying loading conditions, long feeders with multiple laterals and network configurations that are not documented. This process enables the use of a number of techniques to isolate the location of the fault with as much accuracy as the technology allows.

The fault intelligence may further raise fault events. Specifically, this process may create and publish fault events to the events bus once a fault has been detected, classified, categorized, characterized and isolated. This process may also be responsible for collecting, filtering, collating and de-duplicating faults so that an individual fault event is raised rather than a deluge based on the raw events that are typical during a failure. Finally, the fault intelligence may log fault events to the event log database.

FIG. 15 illustrates a flow diagram 1500 of the Meta-data Management processes. Meta-data management processes may include: point list management; and communication connectivity & protocol management; and element naming & translation; sensor calibration factor management; and real time grid topology data management. The base connectivity extract application may request base connectivity data, as shown at block 1502. The Geographic Information Systems (GIS) may receive the request (block 1510) and send data to the base connectivity extract application (block 1512). The base connectivity extract application may receive the data (block 1504), extract, transform and load data (block 1506) and send base connectivity data to the connectivity data mart (block 1508). The connectivity data mart may thereafter receive the data, as shown at block 1514.

The connectivity data mart may comprise a custom data store that contains the electrical connectivity information of the components of the grid. As shown in FIG. 15, this information may be derived typically from the Geographic Information System (GIS) of the utility, which holds the as built geographical location of the components that make up the grid. The data in this data store describes the hierarchical information about all the components of the grid (substation, feeder, section, segment, branch, t-section, circuit breaker, recloser, switch, etc—basically all the assets). This data store may have the asset and connectivity information as built.

The meta data extract application may request meta data for grid assets, as shown at block 1516. The meta data database may receive the request (block 1524) and send meta data (block 1526). The meta data extract application may receive the meta data (block 1518), extract, transform and load meta data (block 1520), and send the meta data to the CIM data warehouse (block 1522).

The CIM (Common Information Model) data warehouse may then store the data, as shown at block 1528. CIM may prescribe utility standard formats for representing utility data. The INDE smart grid may facilitate the availability of information from the smart grid in a utility standard format. And, the CIM data warehouse may facilitate the conversion of INDE specific data to one or more formats, such as a prescribed utility standard format.

The asset extract application may request information on new assets, as shown at block 1530. The asset registry may receive the request (block 1538) and send information on the new assets (block 1540). The asset extract application may receive the information on the new assets (block 1532), extract transform and load data (block 1534), and send information on the new assets to the CIM data warehouse (block 1536).

The DR connectivity extract application may request DR connectivity data, as shown at block 1542. The operational data bus may send the DR connectivity data request to the marketing database, as shown at block 1548. The marketing database may receive the request (block 1554), extract, transform, load DR connectivity data (block 1556), and send the DR connectivity data (block 1558). The operational data bus may send the DR connectivity data to the DR connectivity extract application (block 1550). The DR connectivity extract application may receive the DR connectivity data (block 1544), and send the DR connectivity data (block 1546) via the operational data bus (block 1552) to the grid state and connectivity DM, which stores the DR connectivity data (block 1560).

FIG. 16 illustrates a flow diagram 1600 of the Notification Agent processes. A notification subscriber may log into a webpage, as shown at block 1602. The notification subscriber may create/modify/delete scenario watch list parameters, as shown at block 1604. The web page may store the created/modified/deleted scenario watch list, as shown at block 1608, and the CIM data warehouse may create a list of data tags, as shown at block 1612. A name translate service may translate the data tags for the historian (block 1614) and send the data tags (block 1616). The web page may send the data tag list (block 1610) via the operational data bus, which receives the data tag list (block 1622) and sends it to the notification agent (block 1624). The notification agent retrieves the list (block 1626), validates and merges the lists (block 1628), and checks the historian for notification scenarios (block 1630). If exceptions matching the scenarios are found (block 1632), a notification is sent (block 1634). The event bus receives the notification (block 1618) and sends it to the notification subscriber (block 1620). The notification subscriber may receive the notification via a preferred medium, such as text, e-mail, telephone call, etc., as shown at block 1606.

FIG. 17 illustrates a flow diagram 1700 of the Collecting Meter Data (AMI) processes. The current collector may request residential meter data, as shown at block 1706. One or more residential meters may collect residential meter data in response to the request (block 1702) and send the residential meter data (block 1704). The current collector may receive the residential meter data (block 1708) and send it to the operational data bus (block 1710). The meter data collection engine may request commercial and industrial meter data, as shown at block 1722. One or more commercial and industrial meters may collect commercial and industrial meter data in response to the request (block 1728) and send the commercial and industrial meter data (block 1730). The meter data collection engine may receive the commercial and industrial meter data (block 1724) and send it to the operational data bus (block 1726).

The operational data bus may receive residential, commercial, and industrial meter data (block 1712) and send the data (block 1714). The data may be received by the meter data repository database (block 1716) or may be received by the billing processor (block 1718), which may in turn be sent to one or more systems, such as a CRM (customer relationship management) system (block 1720).

The observability processes may further include remote asset monitoring processes. Monitoring the assets within a power grid may prove difficult. There may be different portions of the power grid, some of which are very expensive. For example, substations may include power transformers (costing upwards of $1 million), and circuit breakers. Oftentimes, utilities would do little, if anything, in the way of analyzing the assets and maximizing the use of the assets. Instead, the focus of the utility was typically to ensure that the power to the consumer was maintained. Specifically, the utility was focused on scheduled inspections (which would typically occur at pre-determined intervals) or “event-driven” maintenance (which would occur if a fault occurred in a portion of the grid).

Instead of the typical scheduled inspections or “event-driven” maintenance, the remote asset monitoring processes may focus on condition-based maintenance. Specifically, if one portion (or all) of the power grid may be assessed (such as on a periodic or continual basis), the health of the power grid may be improved.

As discussed above, data may be generated at various portions of the power grid and transmitted to (or accessible by) a central authority. The data may then be used by the central authority in order to determine the health of the grid. Apart from analyzing the health of the grid, a central authority may perform utilization monitoring. Typically, equipment in the power grid is operated using considerable safety margins. One of the reasons for this is that utility companies are conservative by nature and seek to maintain power to the consumer within a wide margin of error. Another reason for this is that the utility companies monitoring the grid may not be aware of the extent a piece of equipment in the power grid is being utilized. For example, if a power company is transmitting power through a particular feeder circuit, the power company may not have a means by which to know if the transmitted power is near the limit of the feeder circuit (for example, the feeder circuit may become excessively heated). Because of this, the utility companies may be underutilizing one or more portions of the power grid.

Utilities also typically spend a considerable amount of money to add capacity to the power grid since the load on the power grid has been growing (e.g., the amount of power consumed has been increasing). Because of the Utilities' ignorance, Utilities will upgrade the power grid unnecessarily. For example, feeder circuits that are not operating near capacity may nonetheless be upgraded by reconductoring (e.g., bigger wires are laid in the feeder circuits), or additional feeder circuits may be laid. This cost alone is considerable.

The remote asset monitoring processes may monitor various aspects of the power grid, such as: (1) analyzing current asset health of one or more portions of the grid; (2) analyzing future asset health of one or more portions of the grid; and (3) analyzing utilization of one or more portions of the grid. First, one or more sensors may measure and transmit to remote asset monitoring processes in order to determine the current health of the particular portion of the grid. For example, a sensor on a power transform may provide an indicator of its health by measuring the dissolved gases on the transformer. The remote asset monitoring processes may then use analytical tools to determine if the particular portion of the grid (such as the power transform is healthy or not healthy). If the particular portion of the grid is not healthy, the particular portion of the grid may be fixed.

Moreover, the remote asset monitoring processes may analyze data generated from portions of the grid in order to predict the future asset health of the portions of the grid. There are things that cause stress on electrical components. The stress factors may not necessarily be constant and may be intermittent. The sensors may provide an indicator of the stress on a particular portion of the power grid. The remote asset monitoring processes may log the stress measurements, as indicated by the sensor data, and may analyze the stress measurement to predict the future health of the portion of the power grid. For example, the remote asset monitoring processes may use trend analysis in order to predict when the particular portion of the grid may fail, and may schedule maintenance in advance of (or concurrently with) the time when the particular portion of the grid may fail. In this way, the remote asset monitoring processes may predict the life of a particular portion of the grid, and thus determine if the life of that portion of the grid is too short (e.g., is that portion of the grid being used up too quickly).

Further, the remote asset monitoring processes may analyze the utilization of a portion of the power grid in order to better manage the power grid. For example, the remote asset monitoring processes may analyze a feeder circuit to determine its operating capacity. In this feeder circuit example, the remote asset monitoring processes may determine that the feeder circuit is currently being operated at 70%. The remote asset monitoring processes may further recommend that the particular feeder circuit may be operated at a higher percentage (such as 90%), while still maintaining acceptable safety margins. The remote asset monitoring processes may thus enable an effective increase in capacity simply through analyzing the utilization.

Methodology for Determining Specific Technical Architecture

There are various methodologies for determining the specific technical architecture that may use one, some, or all of the elements of the INDE Reference Architecture. The methodology may include a plurality of steps. First, a baseline step may be performed in generating documentation of the as-is state of the utility, and a readiness assessment for transition to a Smart Grid. Second, a requirements definition step may be performed in generating the definition of the to-be state and the detailed requirements to get to this state.

Third, a solution development step may be performed in generating the definition of the solution architecture components that will enable the Smart Grid including the measurement, monitoring and control. For the INDE architecture, this may include the measuring devices, the communication network to pass data from the devices to the INDE CORE 120 applications, the INDE CORE 120 applications to persist and react to the data, analytical applications to interpret the data, the data architecture to model the measured and interpreted data, the integration architecture to exchange data and information between INDE and utility systems, the technology infrastructure to run the various applications and databases and the standards that may be followed to enable an industry standard portable and efficient solution.

Fourth, a value modeling may be performed in generating the definition of key performance indicators and success factors for the Smart Grid and the implementation of the ability to measure and rate the system performance against the desired performance factors. The disclosure above relates to the Architecture development aspect of step 3.

FIG. 19 illustrates an example of a blueprint progress flow graphic. Specifically, FIG. 19 illustrates a process flow of the steps that may be undertaken to define the smart grid requirements and the steps that may be executed to implement the smart grid. The smart grid development process may begin with a smart grid vision development, which may outline the overall goals of the project that may lead to the smart grid roadmapping process. The roadmapping process may lead to blueprinting and to value modeling.

Blueprinting may provide a methodical approach to the definition of the smart grid in the context of the entire utility enterprise. Blueprinting may include an overall roadmap, which may lead to a baseline and systems evaluation (BASE) and to a requirements definition and analytics selection (RDAS). The RDAS process may create the detailed definition of the utility's specific smart grid.

The BASE process may establish the starting point for the utility, in terms of systems, networks, devices, and applications to support smart grid capabilities. The first part of the process is to develop a systems inventory of the grid, which may include: grid structure (such as generation, transmission lines, transmission substations, sub transmission lines, distribution substations, distribution feeders, voltage classes); grid devices (such as switches, reclosers, capacitors, regulators, voltage drop compensators, feeder inter-ties); substation automation (such as IEDs, substation LANs, instrumentation, station RTUs/computers); distribution automation (such as capacitor and switch control; fault isolation and load rollover controls; LTC coordination systems; DMS; Demand Response Management System); and grid sensors (such as sensor types, amounts, uses, and counts on distribution grids, on transmission lines and in substations); etc. Once the inventory is complete, an evaluation of the utility against a high level smart grid readiness model may be created. An as-is dataflow model and a systems diagram may also be created.

The architecture configuration (ARC) process may develop a preliminary smart grid technical architecture for the utility by combining the information from the BASE process, requirements and constraints from the RDAS process and the INDE Reference Architecture to produce a technical architecture that meets the specific needs of the utility and that takes advantage of the appropriate legacy systems and that conforms to the constraints that exist at the utility. Use of the INDE Reference Architecture may avoid the need to invent a custom architecture and ensures that accumulated experience and best practices are applied to the development of the solution. It may also ensure that the solution can make maximum use of reusable smart grid assets.

The sensor network architecture configuration (SNARC) process may provide a framework for making the series of decisions that define the architecture of a distributed sensor network for smart grid support. The framework may be structured as a series of decision trees, each oriented to a specific aspect of sensor network architecture. Once the decisions have been made, a sensor network architecture diagram may be created.

The sensor allocation via T-section recursion (SATSECTR) process may provide a framework for determining how many sensors should be placed on the distribution grid to obtain a given level of observability, subject to cost constraints. This process may also determine the sensor types and locations.

The solution element evaluation and components template (SELECT) process may provide a framework for evaluation of solution component types and provides a design template for each component class. The template may contain a reference model for specifications for each of the solution elements. These templates may then be used to request vendor quotations and to support vendor/product evaluations.

The upgrade planning for applications and networks (UPLAN) process may provide for development of a plan to upgrade of existing utility systems, applications, and networks to be ready for integration into a smart grid solution. The risk assessment and management planning (RAMP) process may provide an assessment of risk associated with specific elements of the smart grid solution created in the ARC process. The UPLAN process may assesses the level or risk for identified risk elements and provides an action plan to reduce the risk before the utility commits to a build-out. The change analysis and management planning (CHAMP) process may analyze the process and organizational changes that may be needed for the utility to realize the value of the smart grid investment and may provide a high-level management plan to carry out these changes in a manner synchronized with the smart grid deployment. The CHAMP process may result in a blueprint being generated.

The roadmap in the value modeling process may lead to specifying value metrics, which may lead to estimation of cost and benefits. The estimation may lead to the building of one or more cases, such as a rate case and business case, which in turn may lead to a case closure. The output of blueprinting and the value modeling may be sent to the utility for approval, which may result in utility system upgrades and smart grid deployments and risk reduction activities, after which the grid may be designed, built and tested, and then operated.

FIG. 21 is block diagram illustrating an alternative example smart grid system 2100 configured for a utility system, such as an electric utility system. In one embodiment, the smart grid system 2100 may include a plurality of meters 2102 that may be configured to measure and transmit data regarding utility usage, such as power, water, natural gas, etc. The grid 2100 may include user stations 2104 and administrator stations 2106 each connected to a network 2108. The user stations 2104 may include computers having a processor and memory allowing a user to interface with the grid 2100 in order to retrieve data, control operations (with authorization), or use the grid 2100 in any other informational or control activity. Each administrator station 2106 may include a processor and memory and be used by grid administrators in an IT capacity for managing and protecting the flow of information throughout the grid 2100.

Table 6 below describes the various functional components of the smart grid 2100.

TABLE 6 Alternative Smart Grid Configuration Components Utility Enterprise A large-scale, integrated application-software packages that use the System 2112 computational, data storage, and data transmission power of modern information technology to support business processes, information flows, reporting, and data analytics within and between complex organizations. Secure Head-End Hardened head-end using security controls 2114 MDM 2116 Meter data management system functions (e.g., Lodestar) OMS 2118 Outage management system DMS 2120 Distribution management system Billing 2122 Customer billing system GIS 2124 Graphical Information System RTD & Line Server Real Time Data server 2126 User Interface The space where interaction between humans and machines occurs. Layer 2128 Visualization & Presents information from diverse sources in a unified visual way Portal 2130 Firewall 2132 A device that permits or denies network transmissions based upon a set of rules. Firewalls are used to protect networks from unauthorized access while permitting legitimate communications to pass. Login Page 2134 A place to enter a user name and password and click on the log in button to obtain access. Business Object & A business object is a type of an intelligible entity being an actor Crystal Reports inside the business layer in an n-layered architecture of object- 2136 oriented computer programs. Crystal Reports is a business intelligence application used to design and generate reports from a wide range of data sources. Business Layer A compartment in a multi-tier architecture that separates the Logic 2138 business logic from the data access layer and the user interface layer. BPEL 2140 Business Process Execution Language (BPEL), short for Web Services Business Process Execution Language (WS-BPEL) is an OASIS standard executable language for specifying actions within business processes with web services. Processes in Business Process Execution Language export and import information by using web service interfaces exclusively. OSB 2142 Oracle Service Bus WEBLOGIC A Java EE platform product-family that includes: a Java EE Server 2144 application server, WebLogic Application Server; an enterprise portal, WebLogic Portal; an Enterprise Application Integration platform; a transaction server and infrastructure, WebLogic Tuxedo; a telecommunication platform, WebLogic Communication Platform; and an HTTP web server. Custom J2EE A customized Java 2 Platform, Enterprise Edition (J2EE) web Service 2146 service CEP Server 2148 Server dedicated to handling event message streams - purpose of a dedicated bus is to provide high bandwidth and low latency for highly bursty event message floods. The server may be in the form of an XML message. Other types of messages may be used. Policy Server 2150 A security component of a policy-based network that provides authorization services and facilitates tracking and control of files. The policy server accepts access control requests, processes them against a formal set of statements that define how the network's resources are to be allocated among its clients (known as the policy), and returns access control responses. In a policy-based network, the resources can be allocated by the policy server based on the time of day, client authorization privileges, availability of network resources, and any other factors that the network manager may specify when composing the policy. With respect to files and documents, the policy server can allow or deny access, control the extent to which a client can use the document (for example, one- time printing or downloading only), track client use patterns, log changes or modifications, provide automatic updates, eliminate unnecessary redundancy, minimize the need to re-issue documents, and delete outdated data. Coherence Server An application that provides replicated and distributed (partitioned) 2152 data management and caching services. ODI 2154 Oracle Data Integrator Data Access Layer A layer which provides simplified access to data stored in 2156 persistent storage of some kind, such as an entity-relational database. FTP 2158 File Transfer Protocol File Server 2160 A server storing flat files, not XML or JMS messages JMS Server 2162 Java Message Service Server that lets the Java Message Service (JMS) API send messages between two or more clients. JMS is a part of the Java Platform, Enterprise Edition, and is defined by a specification developed under the Java Community Process as JSR 914. It is a messaging standard that allows application components based on the Java 2 Platform, Enterprise Edition (J2EE) to create, send, receive, and read messages. It allows the communication between different components of a distributed application to be loosely coupled, reliable, and asynchronous. DB Adapter 2164 Database Adapter - The database adapter enables a BPEL process to communicate with Oracle databases or third-party databases through JDBC. Databases 2166 The collection of the following databases: Queue, Oracle 11g, Oracle XE, PI, Coherence, User. Queue 2168 A database staging area that contains messages that have been sent and are waiting to be read. Oracle 11g 2170 A database that facilitates the management of transaction processing, business intelligence, and content management applications. Protects from server failure, site failure, human error, and reduces planned downtime. Secures data and enables compliance with unique row-level security, fine-grained auditing, transparent data encryption, and total recall of data. High- performance data warehousing, online analytic processing, and data mining. Easily manages entire lifecycle of information for the largest of databases. Oracle XE 2172 Oracle Database 10g Express Edition (Oracle Database XE) is an entry-level, small-footprint database. PI 2174 A database supporting SAP NetWeaver Process Integration (SAP PI) is SAP's enterprise application integration (EAI) software. SAP PI is a component of the NetWeaver product group used to facilitate the exchange of information among a company's internal software and systems and those of external parties. Coherence database A database that organizes, stores, and retrieves large amounts of (DB) 2176 data to help the Coherence application provide replicated and distributed (partitioned) data management and caching services. User DB 2178 A database containing User data, e.g., usernames System A combination of hardware and software used to monitor and Management administer a network. Network 2180 IONIX 2182 All users to automate and manage IT physical, virtual, or cloud computing infrastructure. OSB Console 2184 Oracle Service Bus BPEL Console Business Process Execution Language Console 2186 WebLogic Console The BEA WebLogic Server Administration Console is a Web 2188 browser-based, graphical user interface you use to manage a WebLogic Server domain. CEP Controls 2190 Complex Event Processor Controls ODI Console 2192 Oracle Data Integrator Console SIEM 2194 Security Information Event Manager

As described with regard to FIG. 1, the CEP Console 2190 may operate similar to the CEP processing bus 147. In one example, the CEP Console 2190 may be configured to operate with the STEM 2194 and generate a residual risk event message 2010 that may be provided to the SIEM 194 allowing generation of a residual characterization message 2012.

FIG. 22 is an operational flow diagram of a smart grid, such as that shown in FIG. 1. The complex event processing bus 147 may receive non-IT data 2000 (2200), which may include non-traditional security data. The received data 2000 may be filtered or pre-processed by data filter module 2002 (2202). Pre-processing may include disregarding non-IT data in the CEP bus 147 that fails to meet a predetermined level of relevance to one of a plurality of risk-related events. The rules engine module 2004 may apply a particular set of rules to the filtered data 2003 to determine a probability level that an event is being caused by malicious activity (2204). The rules engine 2004 may access the historical correlation module 2006 in one embodiment to verify that such activity is not likely due to causes other than malicious activity (2206). Based on the historical data from the historical correlation module 2006, the rules engine 2004 may generate a residual risk event message 2008 and provide it to the SIEM 162 (2208). The SIEM 162 may also receive IT event data 2010 (2210). The SIEM 162 may generate a risk characterization message 2012 based on the residual risk event message 2008 and IT event data 2010 (2212) which may be further reviewed by those overseeing network operations of INDE or other smart grid implementing the particular configuration.

While various embodiments of the innovation have been described, it will be apparent to those of ordinary skill in the art that many more embodiments and implementations are possible within the scope of the innovation. Accordingly, the innovation is not to be restricted except in light of the attached claims and their equivalents. 

The invention claimed is:
 1. A method of characterizing malicious activity in an intelligent utility grid system, the method executable by a computer having at least one processor and at least one memory, comprising: receiving, by the at least one processor, information-technology (IT) data including IT-related activity from the intelligent grid system; receiving, by the at least one processor, non-IT data including location-specific event data from a plurality of electronic sources; grid analog measurements comprising phasor measurements; and a list of high-value targets and corresponding geographic locations; pre-processing, by the at least one processor, the non-IT data including: disregarding the non-IT data failing to meet a predetermined level of relevance to one of a plurality of risk-related events; applying, by the at least one processor, a plurality of rules to the pre-processed non-IT data comprising: associating an undesired event with the IT-related activity; determining a probability that the undesired event is indicative of malicious activity including comparing predetermined criteria to the non-IT data to generate one of a plurality of probability levels as a sum of: (1) a product of a probability of occurrence of an intentional malicious attack and a probability of existence of a vulnerability exploitable by the intentional malicious attack; and (2) a product of a probability of occurrence of an unexpected hazard and a probability of existence of a vulnerability associated with the unexpected hazard, where the intentional malicious attack and the unexpected hazard comprise mutually independent events; and applying, by the at least one processor, a risk characterization to the undesired event based on the probability level and the IT-related activity.
 2. The method of claim 1, where the undesired event has not yet occurred.
 3. The method of claim 1, where the risk characterization comprises an engineering risk or a security risk.
 4. The method of claim 1, further comprising: generating a risk characterization message having the risk characterization of the undesired event; and sending the risk characterization message to a system administrator.
 5. The method of claim 1, where the probability level is generated based on a co-existence of a threat and a corresponding vulnerability found in the IT-related or non-IT data that is exploitable by the threat.
 6. The method of claim 4, where the risk characterization message including the probability level and an area at risk selected from the groups consisting of security, engineering, and communications.
 7. The method of claim 1, where the non-IT data further includes historical data retrieved from: event logs, geographical locations associated with corresponding parts of the intelligent utility grid system, and from operational data; and where applying the plurality of rules includes comparing the IT-related activity to the historical data.
 8. The method of claim 1, where at least part of the IT-related activity comprises an event message from a smart meter; and applying the risk characterization includes determining an area within the intelligent utility grid system where the malicious activity is occurring.
 9. The method of claim 1, where the electronic sources of non-IT data include one or a combination of the following inputs: a weather feed; a disturbance recorder feed; a digital fault recorder feed; a harmonic recorder feed; a power quality monitor feed; a device status; a connectivity state; a control limit; US CERT feeds; GPS feeds; a Power Management Unit (PMU) feed; sensor feeds; load forecasts; and renewable generation forecasts.
 10. A system for characterizing malicious activity in an intelligent utility grid system, comprising: a system storage in which to store a database including a plurality of rules; a collector operable to collect and store in the system storage information-technology (IT) data including IT-related activity from the intelligent grid system; a complex event processing (CEP) bus operable to receive non-IT data including location-specific event data from a plurality of electronic sources, the CEP bus further operable to disregard the non-IT data failing to meet a predetermined level of relevance to one of a plurality of risk-related events, where the non-IT data further includes historical data retrieved from: event logs, geographical locations associated with corresponding parts of the intelligent utility grid system, and from operational data; a processor operable to apply the plurality of rules to the relevant non-IT data to: associate an undesired event with the IT-related activity; and determine a probability that the undesired event is indicative of malicious activity including comparing predetermined criteria to the non-IT data to generate one of a plurality of probability levels as a sum of: (1) a product of a probability of occurrence of a intentional malicious attack and a probability of existence of a vulnerability exploitable by the intentional malicious attack; and (2) a product of a probability of occurrence of an unexpected hazard and a probability of existence of a vulnerability associated with the unexpected hazard, where the intentional malicious attack and the unexpected hazard comprise mutually independent events; and the processor further to apply a risk characterization to the undesired event based on the probability level and the IT-related activity.
 11. The system of claim 10, further comprising the CEP bus coupled with one or a combination of the following electronic sources of non-IT data: a Web-crawling device; a search engine-capable computing device; a Web-access device; a GPS device; a social-media-thread monitoring device; a thermometer; and an emergency response communicator.
 12. The system of claim 10, where the undesired event has not yet occurred, and where the risk characterization comprises an engineering risk or a security risk.
 13. The system of claim 10, where to apply the plurality of rules, the processor is further configured to: generate a risk characterization message having the risk characterization of the undesired event; and send the risk characterization message to a system administrator.
 14. The system of claim 10, where the probability level is generated based on a co-existence of a threat and a corresponding vulnerability found in the IT-related or non-IT data that is exploitable by the threat.
 15. The system of claim 10, where the processor applies the plurality of rules by comparing the IT-related activity to the historical data.
 16. The system of claim 10, where at least part of the IT-related activity comprises an event message from a smart meter; and the processor applies the risk characterization by determining an area within the intelligent utility grid system where the malicious activity is occurring.
 17. The system of claim 10, where the non-IT data further includes: grid analog measurements comprising phasor measurements; and a list of high-value targets and corresponding geographic locations.
 18. A non-transitory computer-readable storage medium comprising a set of instructions for characterizing malicious activity in an intelligent utility grid system executable by a computer having a processor and memory, the computer-readable medium comprising: instructions to receive information-technology (IT) data including IT-related activity from the intelligent grid system; instructions to receive non-IT data including location-specific event data from a plurality of electronic sources; grid analog measurements comprising phasor measurements; and a list of high-value targets and corresponding geographic locations; instructions to pre-process the non-IT data including: disregarding the non-IT data failing to meet a predetermined level of relevance to one of a plurality of risk-related events; instructions to apply a plurality of rules to the pre-processed non-IT data to: associate an undesired event with reference to the IT-related activity; and determine a probability that the undesired event is indicative of malicious activity including comparing predetermined criteria to the non-IT data to generate one of a plurality of probability levels as a sum of: (1) a product of a probability of occurrence of a intentional malicious attack and a probability of existence of a vulnerability exploitable by the intentional malicious attack; and (2) a product of a probability of occurrence of an unexpected hazard and a probability of existence of a vulnerability associated with the unexpected hazard, where the intentional malicious attack and the unexpected hazard comprise mutually independent events; and instructions to apply a risk characterization to the undesired event based on the probability level and the IT-related activity.
 19. The computer-readable storage medium of claim 18, where at least part of the IT-related activity comprises an event message from a smart meter; and to apply the risk characterization, the instructions to determine an area within the intelligent utility grid system where the malicious activity is occurring.
 20. The computer-readable storage medium of claim 18, where the non-IT data further includes historical data retrieved from: event logs, geographical locations associated with corresponding parts of the intelligent utility grid system, and from operational data; and to apply the plurality of rules, the instructions further to compare the IT-related activity to the historical data.
 21. The computer-readable storage medium of claim 18, where the risk characterization comprises an engineering risk or a security risk.
 22. The computer-readable storage medium of claim 18, where the criteria includes one or a combination of: temperature, dollars, social networking statistics.
 23. The computer-readable storage medium of claim 22, where the probability level is generated based on a co-existence of a threat and a corresponding vulnerability found in the IT-related or non-IT data that is exploitable by the threat. 